Subjects -> MANUFACTURING AND TECHNOLOGY (Total: 363 journals)
    - CERAMICS, GLASS AND POTTERY (31 journals)
    - MACHINERY (34 journals)
    - MANUFACTURING AND TECHNOLOGY (223 journals)
    - METROLOGY AND STANDARDIZATION (6 journals)
    - PACKAGING (19 journals)
    - PAINTS AND PROTECTIVE COATINGS (4 journals)
    - PLASTICS (42 journals)
    - RUBBER (4 journals)

MANUFACTURING AND TECHNOLOGY (223 journals)                  1 2     

Showing 1 - 73 of 73 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 27)
Additive Manufacturing     Hybrid Journal   (Followers: 18)
Additive Manufacturing Letters     Open Access   (Followers: 3)
Advanced Composites and Hybrid Materials     Hybrid Journal   (Followers: 2)
Advanced Industrial and Engineering Polymer Research     Open Access   (Followers: 4)
Advanced Manufacturing: Polymer & Composites Science     Open Access   (Followers: 39)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 9)
Advances in Industrial and Manufacturing Engineering     Open Access   (Followers: 4)
Advances in Manufacturing     Hybrid Journal   (Followers: 9)
Advances in Manufacturing Science and Technology     Open Access   (Followers: 10)
Advances in Materials and Processing Technologies     Hybrid Journal   (Followers: 1)
Advances in Technology Innovation     Open Access   (Followers: 5)
Afrique Science : Revue Internationale des Sciences et Technologie     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 22)
American Journal of Sensor Technology     Open Access   (Followers: 2)
Appita Journal: Journal of the Technical Association of the Australian and New Zealand Pulp and Paper Industry     Full-text available via subscription   (Followers: 6)
Applied Ergonomics     Hybrid Journal   (Followers: 17)
Asia Pacific Biotech News     Hybrid Journal   (Followers: 3)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Australian Journal of Learning Difficulties     Hybrid Journal   (Followers: 9)
Australian TAFE Teacher     Full-text available via subscription   (Followers: 4)
Behavioral and Cognitive Neuroscience Reviews     Hybrid Journal   (Followers: 25)
Bio-Design and Manufacturing     Hybrid Journal  
Biomanufacturing Reviews     Full-text available via subscription  
Biotechnology     Open Access   (Followers: 7)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Bulletin of Science, Technology & Society     Hybrid Journal   (Followers: 9)
Centaurus     Hybrid Journal   (Followers: 7)
China Foundry     Open Access  
Circuit World     Hybrid Journal   (Followers: 16)
Clay Technology     Full-text available via subscription  
Cold Regions Science and Technology     Hybrid Journal   (Followers: 2)
Comparative Technology Transfer and Society     Full-text available via subscription   (Followers: 4)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 27)
Composites Science and Technology     Hybrid Journal   (Followers: 158)
Computer-Aided Design and Applications     Hybrid Journal   (Followers: 6)
Conference Quality Production Improvement     Open Access  
Control Theory and Informatics     Open Access   (Followers: 9)
Control Theory and Technology     Hybrid Journal   (Followers: 2)
Cryoletters     Full-text available via subscription   (Followers: 4)
Current Protocols in Essential Laboratory Techniques     Full-text available via subscription  
Current Research in Nanotechnology     Open Access   (Followers: 23)
Decision Making : Applications in Management and Engineering     Open Access   (Followers: 1)
Decision Making in Manufacturing and Services     Open Access   (Followers: 2)
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 33)
Design Studies     Hybrid Journal   (Followers: 34)
Economics of Innovation and New Technology     Hybrid Journal   (Followers: 19)
Emerging Materials Research     Hybrid Journal   (Followers: 1)
Environmental Technology     Hybrid Journal   (Followers: 2)
Fibers     Open Access   (Followers: 4)
Fibers and Polymers     Full-text available via subscription   (Followers: 4)
Foresight     Hybrid Journal   (Followers: 7)
FORMakademisk - forskningstidsskrift for design og designdidaktikk     Open Access   (Followers: 2)
Futures     Hybrid Journal   (Followers: 15)
Gender, Technology and Development     Hybrid Journal   (Followers: 14)
Green Materials     Hybrid Journal   (Followers: 3)
History and Technology: An International Journal     Hybrid Journal   (Followers: 11)
History of Science and Technology     Open Access   (Followers: 2)
Human Factors in Design     Open Access   (Followers: 10)
i+Diseño : Revista científico-académica internacional de Innovación, Investigación y Desarrollo en Diseño     Open Access  
IEEE Engineering in Medicine and Biology Magazine     Full-text available via subscription   (Followers: 6)
IEEE Open Journal of Nanotechnology     Open Access   (Followers: 1)
IET Collaborative Intelligent Manufacturing     Open Access   (Followers: 1)
IETE Journal of Research     Open Access   (Followers: 10)
IETE Technical Review     Open Access   (Followers: 9)
IFAC Journal of Systems and Control     Hybrid Journal   (Followers: 1)
Indian Journal of Radio & Space Physics (IJRSP)     Open Access   (Followers: 49)
Información Tecnológica     Open Access  
Innovation: Management, Policy & Practice     Hybrid Journal   (Followers: 16)
Innovation: The European Journal of Social Science Research     Hybrid Journal   (Followers: 10)
Innovations : Technology, Governance, Globalization     Hybrid Journal   (Followers: 10)
Integrating Materials and Manufacturing Innovation     Open Access   (Followers: 7)
International Journal for Quality Research     Open Access   (Followers: 4)
International Journal for the History of Engineering and Technology     Hybrid Journal   (Followers: 2)
International Journal of Additive and Subtractive Materials Manufacturing     Hybrid Journal   (Followers: 8)
International Journal of Advanced Design and Manufacturing Technology     Open Access   (Followers: 7)
International Journal of Automation and Logistics     Hybrid Journal   (Followers: 3)
International Journal of Bifurcation and Chaos     Hybrid Journal   (Followers: 4)
International Journal of Business and Systems Research     Hybrid Journal   (Followers: 1)
International Journal of Concrete Technology     Full-text available via subscription   (Followers: 1)
International Journal of Design     Open Access   (Followers: 24)
International Journal of Design Creativity and Innovation     Hybrid Journal   (Followers: 2)
International Journal of Digital Enterprise Technology     Hybrid Journal   (Followers: 1)
International Journal of Embedded Systems and Emerging Technologies     Full-text available via subscription   (Followers: 3)
International Journal of Energy Technology and Policy     Hybrid Journal   (Followers: 6)
International Journal of Engineering and Manufacturing     Open Access   (Followers: 3)
International Journal of Engineering Materials and Manufacture     Open Access   (Followers: 2)
International Journal of Experimental Design and Process Optimisation     Hybrid Journal   (Followers: 5)
International Journal of Information Acquisition     Hybrid Journal   (Followers: 1)
International Journal of Innovation and Technology Management     Hybrid Journal   (Followers: 10)
International Journal of Innovation Science     Hybrid Journal   (Followers: 9)
International Journal of Instructional Technology and Educational Studies     Open Access   (Followers: 1)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 13)
International Journal of Law and Information Technology     Hybrid Journal   (Followers: 7)
International Journal of Learning Technology     Hybrid Journal   (Followers: 8)
International Journal of Lightweight Materials and Manufacture     Open Access  
International Journal of Manufacturing Engineering     Open Access   (Followers: 4)
International Journal of Manufacturing, Materials, and Mechanical Engineering     Full-text available via subscription   (Followers: 17)
International journal of materials research     Full-text available via subscription   (Followers: 2)
International Journal of Mathematical Education in Science and Technology     Hybrid Journal   (Followers: 9)
International Journal of Nano and Biomaterials     Hybrid Journal   (Followers: 8)
International Journal of Physical Modelling in Geotechnics     Hybrid Journal   (Followers: 4)
International Journal of Planning and Scheduling     Hybrid Journal   (Followers: 2)
International Journal of Power and Energy Systems     Full-text available via subscription   (Followers: 1)
International Journal of Precision Engineering and Manufacturing-Green Technology     Hybrid Journal   (Followers: 2)
International Journal of Production Management and Engineering     Open Access   (Followers: 4)
International Journal of Quality and Innovation     Hybrid Journal   (Followers: 6)
International Journal of Quality Engineering and Technology     Hybrid Journal   (Followers: 4)
International Journal of Service and Computing Oriented Manufacturing     Hybrid Journal   (Followers: 2)
International Journal of Social and Humanistic Computing     Hybrid Journal  
International Journal of System of Systems Engineering     Hybrid Journal   (Followers: 3)
International Journal of Technoentrepreneurship     Hybrid Journal   (Followers: 1)
International Journal of Technological Learning, Innovation and Development     Hybrid Journal   (Followers: 6)
International Journal of Technology and Design Education     Hybrid Journal   (Followers: 11)
International Journal of Technology and Globalisation     Hybrid Journal   (Followers: 3)
International Journal of Technology Intelligence and Planning     Hybrid Journal  
International Journal of Technology Management     Hybrid Journal   (Followers: 4)
International Journal of Technology Marketing     Hybrid Journal   (Followers: 3)
International Journal of Technology Transfer and Commercialisation     Hybrid Journal   (Followers: 2)
International Journal of Technology, Policy and Management     Hybrid Journal   (Followers: 2)
International Journal of Telecommunications & Emerging Technologies     Full-text available via subscription   (Followers: 1)
International Journal of Vehicle Autonomous Systems     Hybrid Journal  
International Journal of Vehicle Design     Hybrid Journal   (Followers: 6)
International Wood Products Journal     Hybrid Journal   (Followers: 1)
Invention Disclosure     Open Access   (Followers: 1)
ITL - International Journal of Applied Linguistics     Full-text available via subscription   (Followers: 15)
Journal of Analytical Science & Technology     Open Access   (Followers: 4)
Journal of Applied Sciences     Open Access   (Followers: 4)
Journal of Control & Instrumentation     Full-text available via subscription   (Followers: 18)
Journal of Design Research     Hybrid Journal   (Followers: 15)
Journal of Energy, Mechanical, Material and Manufacturing Engineering     Open Access   (Followers: 3)
Journal of Engineered Fibers and Fabrics     Open Access  
Journal of Enterprise Transformation     Hybrid Journal   (Followers: 1)
Journal of Hazardous Materials Advances     Open Access  
Journal of High Technology Management Research     Hybrid Journal   (Followers: 2)
Journal of Industrial and Production Engineering     Hybrid Journal   (Followers: 4)
Journal of large-scale research facilities JLSRF     Open Access  
Journal of Manufacturing and Materials Processing     Open Access  
Journal of Materials Science Research     Open Access   (Followers: 8)
Journal of Micro-Bio Robotics     Hybrid Journal  
Journal of Micromanufacturing     Hybrid Journal  
Journal of Nanobiotechnology     Open Access   (Followers: 4)
Journal of Operations and Supply Chain Management     Open Access   (Followers: 6)
Journal of Production Research & Management     Full-text available via subscription   (Followers: 3)
Journal of Remanufacturing     Open Access   (Followers: 3)
Journal of Scientific and Industrial Research (JSIR)     Open Access   (Followers: 10)
Journal of Sensor Technology     Open Access   (Followers: 3)
Journal of Sensors and Sensor Systems     Open Access   (Followers: 12)
Journal of Sustainable Metallurgy     Hybrid Journal   (Followers: 4)
Journal of Technological Possibilism     Open Access  
Journal of Technology in Human Services     Hybrid Journal   (Followers: 3)
Journal of Technology Management for Growing Economies     Open Access   (Followers: 3)
Journal of Technology Management in China     Hybrid Journal   (Followers: 1)
Journal of the Chinese Institute of Industrial Engineers     Hybrid Journal  
Journal of The Royal Society Interface     Full-text available via subscription   (Followers: 9)
Journal of Urban Technology     Hybrid Journal   (Followers: 5)
Journal of Urban Technology and Sustainability     Open Access  
Jurnal Energi Dan Manufaktur     Open Access  
Lasers in Manufacturing and Materials Processing     Full-text available via subscription   (Followers: 2)
Leibniz Transactions on Embedded Systems     Open Access  
Lightweight Design     Hybrid Journal   (Followers: 4)
Management and Production Engineering Review     Open Access   (Followers: 1)
Manufacturing Letters     Full-text available via subscription   (Followers: 2)
Manufacturing Review     Open Access   (Followers: 1)
Manufacturing Science and Technology     Open Access   (Followers: 3)
Materials Circular Economy     Hybrid Journal  
Materials Science and Engineering: B     Hybrid Journal   (Followers: 22)
Materials testing. Materialprüfung     Full-text available via subscription   (Followers: 4)
MECCA Journal of Middle European Construction and Design of Cars     Open Access  
Mechanics of Soft Materials     Hybrid Journal  
Métodos y Materiales     Open Access   (Followers: 1)
Micro and Nanostructures     Full-text available via subscription   (Followers: 5)
Microgravity Science and Technology     Hybrid Journal   (Followers: 4)
Modern Electronic Materials     Open Access   (Followers: 1)
Multimodal Transportation     Open Access   (Followers: 4)
Nano Select     Open Access  
NanoEthics     Hybrid Journal  
Nanomanufacturing and Metrology     Hybrid Journal   (Followers: 2)
Nature Biotechnology     Full-text available via subscription   (Followers: 531)
NDT & E International     Hybrid Journal   (Followers: 225)
New Techno-Humanities     Open Access   (Followers: 1)
Nonconventional Technologies Review     Open Access  
Perspectives on Global Development and Technology     Hybrid Journal  
Plastics, Rubber and Composites     Hybrid Journal   (Followers: 9)
Procedia CIRP     Open Access   (Followers: 2)
Production & Manufacturing Research     Open Access   (Followers: 2)
Progress in Additive Manufacturing     Hybrid Journal   (Followers: 8)
Progress in Rubber, Plastics and Recycling Technology     Hybrid Journal   (Followers: 1)
Reliability Engineering & System Safety     Hybrid Journal   (Followers: 18)
Remote Sensing Letters     Hybrid Journal   (Followers: 46)
Research Papers Faculty of Materials Science and Technology Slovak University of Technology     Open Access   (Followers: 3)
Revista de Ciências Exatas e Tecnologia     Open Access  
Revista Produção Online     Open Access  
Science and Technology of Advanced Materials     Open Access   (Followers: 7)
Science China Materials     Hybrid Journal   (Followers: 1)
Scientia Canadensis: Canadian Journal of the History of Science, Technology and Medicine / Scientia Canadensis : revue canadienne d'histoire des sciences, des techniques et de la médecine     Open Access   (Followers: 4)
Strategic Design Research Journal     Open Access   (Followers: 1)
Structural Health Monitoring     Hybrid Journal   (Followers: 6)
Superhero Science and Technology     Open Access   (Followers: 3)
Sustainability and Climate Change     Full-text available via subscription   (Followers: 11)

        1 2     

Similar Journals
Journal Cover
Structural Health Monitoring
Journal Prestige (SJR): 0.849
Citation Impact (citeScore): 3
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1475-9217 - ISSN (Online) 1741-3168
Published by Sage Publications Homepage  [1174 journals]
  • Vibration-based Damage Localization and Quantification Framework of
           Large-Scale Truss Structures

    • Free pre-print version: Loading...

      Authors: Olga Markogiannaki, Alexandros Arailopoulos, Dimitrios Giagopoulos, Costas Papadimitriou
      Abstract: Structural Health Monitoring, Ahead of Print.
      The use of structural health monitoring (SHM) systems on a regular basis is critical to achieve early damage detection, avoid unpredicted failures, and perform cost-effective maintenance planning. The main objective of this work is to present a model-based Damage Detection Framework for truss structural systems that uses output-only vibration measurements. Model-based methods provide much more comprehensive information about the condition of the monitored system than the data-driven and also allow the prediction of the location and level of damage. The measured vibration response of a healthy structural system under operational vibrations is employed to tune a parameterized FE model using state-of-the-art FE model updating techniques to obtain an optimal numerical model of the structural system. Based on the optimal FE model, a set of damaged FE models is generated for selected damage scenarios. A damage approximation approach that represents local damage with uniform stiffness reduction is also presented. In the Damage Detection Framework, the vibration data records for both the “healthy” and the “damaged” structure and the results from multiple numerical analysis on the “healthy” and the “damaged” FE models are used. The transmittance functions for the “healthy” and “damaged” states of the structure and the FE models are derived to calculate the damage indicators. Using these indicators, potentially damaged structural members are identified, grouped, and compared to finally locate the specific damaged member. The proposed framework provides both accurate damage localization and damage quantification using a limited number of sensors for unknown input excitation. Herein, the case study used is a laboratory steel truss bridge.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-21T08:38:48Z
      DOI: 10.1177/14759217221100443
       
  • Evaluation of machine learning techniques for structural health monitoring
           using ultrasonic guided waves under varying temperature conditions

    • Free pre-print version: Loading...

      Authors: Abderrahim Abbassi, Niklas Römgens, Franz Ferdinand Tritschel, Nikolai Penner, Raimund Rolfes
      Abstract: Structural Health Monitoring, Ahead of Print.
      The implementation of machine learning methods for structural health monitoring applications has proven to be very powerful, especially in detecting damage and compensating for environmental and operational conditions. The use of guided waves in this area has also shown to be a powerful tool due to their sensitivity to structural changes in the propagation medium. In this work, two strategies for detecting damage and distinguishing their positions and for dealing with temperature variations without an additional classical temperature compensation technique are investigated. For this purpose, four unsupervised dimensionality reduction learning methods were used and compared: Principal Component Analysis, Kernel Principal Component Analysis, t-distributed stochastic neighbour embedding and Autoencoder. The first strategy (score plot) consists of using the latent dimensions directly to distinguish the data points of different states of the structure, and the second (DI plot) proposes a method to use Q- and T2-statistics, which have been proposed in previous work for PCA, computed using the compressed representation of the monitoring data. To this end, monitoring data from intact and damaged states of a 500x500x2 mm plate of carbon-fibre–reinforced polymer recorded by 12 piezoelectric transducers at different temperatures are examined. As a reversible damage model, a 10 mm thick aluminium disc is placed at four different locations on the plate. The results primarily show the success of the methods used with DI plot in detecting damage regardless of varying temperature. The autoencoder in the first strategy also demonstrates promising performance in detecting and distinguishing the position of the damage, even in the presence of varying temperature conditions.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-16T08:06:21Z
      DOI: 10.1177/14759217221107566
       
  • Acoustic emission monitoring of corrosion in steel pipes using Lamb-type
           helical waves

    • Free pre-print version: Loading...

      Authors: Stylianos Livadiotis, Konstantinos Sitaropoulos, Arvin Ebrahimkhanlou, Salvatore Salamone
      Abstract: Structural Health Monitoring, Ahead of Print.
      This paper is concerned with the monitoring of corrosion in steel pipelines using the acoustic emission (AE) technique. Large uniform corrosion in pipes causes significant wall-thickness loss, and the intensity of the AE activity is correlated with the severity of the corrosion. A new approach for considering the helical propagation of corrosion-related AE events is proposed. Specifically, it is suggested that a longer portion of conventional AE hit is considered to account for multiple arrivals of Lamb-type modes traveling helically in the circumference of the pipe known as helical guided waves (HGW). Using the recorded amplitude of these events, a qualitative corrosion monitoring approach is proposed using the b-value analysis. An accelerated corrosion test on a steel pipe instrumented with a network of AE sensors is carried out to validate the proposed approach. Moreover, a numerical study is performed to evaluate the energy variation of HGW during the corrosion process. Both experimental and numerical results suggest that helical Lamb-type AE has the potential to be utilized for corrosion monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-13T08:18:44Z
      DOI: 10.1177/14759217221105644
       
  • Addressing practicalities in multivariate nonlinear regression for
           mitigating environmental and operational variations

    • Free pre-print version: Loading...

      Authors: Callum Roberts, David Garcia Cava, Luis D Avendaño-Valencia
      Abstract: Structural Health Monitoring, Ahead of Print.
      A significant problem associated with the implementation of Vibration-Based Structural Health Monitoring (VSHM) systems originates from the detrimental effects caused by Environmental and Operational Variations (EOVs). The EOVs cause observations from the same structural condition to behave in different manners. As such, this leads to issues when defining a robust baseline state as well as making the discrimination between undamaged and damaged observations more challenging. In order to address these challenges, multivariate nonlinear regression is implemented to account for the EOVs. Damage Sensitive Features (DSFs) are extracted from acceleration data and then are regressed based on environmental and operational parameters. New features are effectively normalised by finding the difference between the measured and predicted values. This process removes the influence of the EOVs in an explicit manner, allowing for more reliable damage detection. While the benefit of the application of different regression methodologies has already been demonstrated in the past, this work addresses a number of practicalities in the implementation of VSHM on real systems. The analysis investigates the selection of the DSF and is investigated alongside another analysis into how the damage detection behaves under varying amounts of input information. Furthermore, a method is proposed to understand and account for a large number of outliers in the training data.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-13T07:25:36Z
      DOI: 10.1177/14759217221091907
       
  • Robust sparse Bayesian learning for broad learning with application to
           high-speed railway track monitoring

    • Free pre-print version: Loading...

      Authors: Chenyue Wang, Jingze Gao, Hui Li, Chao Lin, James L Beck, Yong Huang
      Abstract: Structural Health Monitoring, Ahead of Print.
      In this study, we focus on non-parametric probabilistic modeling for general regression analysis with large amounts of data and present an algorithm called the robust sparse Bayesian broad learning system. Robust sparse Bayesian learning is employed to infer the posterior distribution of the sparse connecting weight parameters in broad learning system. Regardless of the number of candidate features, our algorithm can always produce a compact subset of hidden-layer neurons of almost the same size learned from the data, which allows the algorithm to automatically adjust the model complexity of the network. This algorithm not only solves the regression problem of large amounts of data robustly but also possesses high computational efficiency and low requirements for computing hardware. Moreover, as a Bayesian probabilistic algorithm, it can provide the posterior uncertainty quantification of the predicted output, giving a measure of prediction confidence. The proposed algorithm is verified using simulated data generated by a benchmark function and also applied in non-parametric probabilistic modeling using high-speed railway track monitoring data. The results show that compared with several existing neural network algorithms, our proposed algorithm has strong model robustness, excellent prediction accuracy, and computational efficiency for regression analysis with large amounts of data, and has the potential to be widely used in general regression problems in science and engineering.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-10T02:43:02Z
      DOI: 10.1177/14759217221104224
       
  • Optimized U-shape convolutional neural network with a novel training
           strategy for segmentation of concrete cracks

    • Free pre-print version: Loading...

      Authors: Mohammad Mousavi, Ali Bakhshi
      Abstract: Structural Health Monitoring, Ahead of Print.
      Crack detection is a vital component of structural health monitoring. Several computer vision-based studies have been proposed to conduct crack detection on concrete surfaces, but most cases have difficulties in detecting fine cracks. This study proposes a deep learning-based model for automatic crack detection on the concrete surface. Our proposed model is an encoder–decoder model which uses EfficientNet-B7 as the encoder and U-Net’s modified expansion path as the decoder. To overcome the challenges in the detection of fine cracks, we trained our model with a new training strategy on images extracted from an open-access dataset and achieved a 96.98% F1 score for unseen test data. Moreover, we evaluated our method on CrackForest Dataset and achieved a 97.06% F1 score which outperforms all the existing methods. The robustness of the proposed model is investigated using the various numbers of training data, and the optimal data size for training this model is presented. The results show that although deep learning models acquire a large number of data, this model works with limited data, without any degradation in its performance. Furthermore, the novel training strategy used in this study, significantly improves the model’s accuracy in detecting different types of cracks.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-08T03:38:38Z
      DOI: 10.1177/14759217221105647
       
  • Damage localization with Lamb waves using dense convolutional sparse
           coding network

    • Free pre-print version: Loading...

      Authors: Han Zhang, Jiadong Hua, Jing Lin, Tong Tong
      Abstract: Structural Health Monitoring, Ahead of Print.
      Composite materials are progressively employed in many safety-critical structural applications due to their superior properties. Structural health monitoring techniques based on Lamb waves have been utilized to assess the damages of composite structures. Recently, deep learning algorithms are adopted for damage detection and localization. Identifying valid damage-related features through neural networks is a crucial step in the analysis process. However, most implemented deep learning architectures are still lacking physical interpretability to some extent. In this paper, a dense convolutional sparse coding network (DCSCNet) is presented for Lamb wave-based damage localization in composite structures, providing a possibility to interpret current networks. In DCSCNet, narrowband Hanning windowed toneburst signals are utilized as kernels of the first convolutional layer to learn more meaningful features. Dense connection is theoretically demonstrated in the scope of DCSCNet, which gathers multiple feature maps directly to develop the potential of the network through feature reuse. The multi-layer iterative soft thresholding algorithm with the dense connection is then employed for solving the multi-layer convolutional sparse coding model. Moreover, effective Squeeze-Excitation is introduced as the channel attention module to boost the representational capability of the network. The experimental results demonstrate the high-performance and interpretable characteristics of the proposed DCSCNet, verifying its feasibility and effectiveness in Lamb wave-based damage localization of composite structures.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-07T11:49:56Z
      DOI: 10.1177/14759217221092116
       
  • A study on the use of fundamental antisymmetric-like guided wave for
           health monitoring of elastic–viscoelastic bilayer structures

    • Free pre-print version: Loading...

      Authors: Bikash Ghose, Rabi S Panda, Krishnan Balasubramaniam
      Abstract: Structural Health Monitoring, Ahead of Print.
      This study investigates the ultrasonic guided wave propagation in an elastic–viscoelastic (steel–rubber) bilayer structure. 2D finite element models are developed in the frequency domain to simulate the wave propagation in the steel–rubber bilayer structure. The guided wave A0 mode is generated in the bilayer with a contact L-wave probe and detected with an out-of-plane laser vibrometer. Several wave features, such as amplitude, phase velocity and phase delay, are measured and compared to determine the characteristic changes of the A0 wave mode in the steel layer alone as well as in the bilayer structure. Studies are also performed for the bilayer structure when excited from the steel and rubber surfaces. The amplitude and phase velocity of the A0 mode are reduced in the bilayer compared to the steel layer alone. The phase velocity of the A0 wave mode in the bilayer does not depend on the viscoelastic properties of the rubber layer, rather depends only on the elastic properties of the rubber layer. The viscoelastic rubber layer in the bilayer structure does not sustain any independent wave mode; instead, it carries the A0 mode of the steel layer alone as a modified A0 wave mode in the bilayer structure. A parametric numerical study of the viscoelasticity of the rubber layer in the bilayer structure shows that the attenuation of the modified A0 mode in the bilayer is more affected by the bulk S-wave attenuation than the bulk L-wave attenuation. The rate of attenuation of the modified A0 mode in the bilayer is faster on the rubber surface than on the steel surface. A study on the A0 wave mode interaction with the interfacial disbond between steel and rubber layers is also carried out.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-06T03:42:49Z
      DOI: 10.1177/14759217221104884
       
  • A change-point detection method for detecting and locating the abrupt
           changes in distributions of damage-sensitive features of SHM data, with
           application to structural condition assessment

    • Free pre-print version: Loading...

      Authors: Xinyi Lei, Zhicheng Chen, Hui Li, Shiyin Wei
      Abstract: Structural Health Monitoring, Ahead of Print.
      Damage detection or structural condition assessment is an important objective of structural health monitoring (SHM). The damages or adverse changes in structural conditions can usually be manifested as pattern changes in damage-sensitive features (DSFs) extracted from SHM data; this enables us to shift damage detection to DSF change detection. Online monitoring can accumulate huge amounts of data, finding the changes from the massive DSF data through manual inspection is impractical; thus, automatic detection tools are required. If possible, relevant significance test is also desired to make a rational judgment on the existence of a change. In this sense, the change-point detection technique is an attractive choice, which is increasingly proved to be a powerful change detection tool in various SHM applications. However, existing change-point detection methods in SHM are mainly used for scalar or vector data, thus incapable of detecting changes in features represented by complex data, for example, the probability density functions (PDFs). Detecting abrupt changes in the distributions (represented by PDFs) of the DSF data is of crucial concern in structural condition assessment. However, relevant automatic diagnostic tools have not been well developed in the SHM community. To this end, a novel change-point detection method is developed in the functional data analytic framework for this task. The proposed approach has advantages in detecting changes for massive data and directly handling general PDFs. Considering that the major challenge in PDF-valued data analysis comes from the nonlinear constraints of PDFs, the PDFs are embedded into the Bayes space to develop the detection methodology by using the linear structure of the Bayes space. Comprehensive simulation studies are conducted to validate the effectiveness of the proposed method as well as demonstrate its superiority over the competing method. Finally, a case study involving cable condition assessment of a long-span bridge demonstrates its practical utility in SHM.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-02T10:36:24Z
      DOI: 10.1177/14759217221101320
       
  • A progressive decomposing and double screening strategy of VMD for weak
           fault extraction of hoisting machinery

    • Free pre-print version: Loading...

      Authors: Yang Li, Lei Zou, Chi-Guhn Lee, Feiyun Xu
      Abstract: Structural Health Monitoring, Ahead of Print.
      To alleviate the difficulty of extracting weak fault features of hoisting machinery, a progressive decomposing and double screening strategy of variational mode decomposition (VMD) is presented in this paper. Firstly, the feasibility and effectiveness of extracting fault modes using progressive decomposition strategy is validated through numerical simulation, and it solves the problem of determining the mode number [math] in traditional VMD. Secondly, a new index named energy fluctuation factor (EFF) is proposed. Specifically, EFF is more effective in detecting the signal periodicity compared with the kurtosis and the Shannon entropy (SE), and it is used to optimize the balance parameter [math] of VMD. Thirdly, the criterion of double screening based on the kurtosis and the EFF is given to accurately localize and reconstruct the fault modes, and then Hilbert transform is utilized to demodulate the reconstructed mode. Finally, the numerical simulation and experimental and practical engineering applications verify that the proposed method can accurately extract the modes of weak fault and well solve the problem of determining the key parameters (i.e., [math] and [math] ) of VMD. Furthermore, the superiority of the proposed method is validated by comparing with other fault diagnosis methods.
      Citation: Structural Health Monitoring
      PubDate: 2022-06-01T05:14:28Z
      DOI: 10.1177/14759217221105646
       
  • Gaussian process regression for active sensing probabilistic structural
           health monitoring: experimental assessment across multiple damage and
           loading scenarios

    • Free pre-print version: Loading...

      Authors: Ahmad Amer, Fotis Kopsaftopoulos
      Abstract: Structural Health Monitoring, Ahead of Print.
      In the near future, Structural Health Monitoring (SHM) technologies will be capable of overcoming the drawbacks in the current maintenance and life-cycle management paradigms, namely, cost, increased downtime, less-than-optimal safety management paradigm and the limited applicability of fully-autonomous operations. One of the most challenging tasks is structural damage quantification. Existing quantification techniques face accuracy and/or robustness issues when it comes to varying operating and environmental conditions. In addition, the damage/no-damage paradigm of current industrial frameworks does not offer much information to maintainers on the ground for proper decision-making. In this study, a novel structural damage quantification framework is proposed based on the widely-used Damage Indices (DIs) and Gaussian Process Regression Models (GPRMs) in order to overcome the aforementioned shortcomings. The proposed framework takes a simple approach to the damage quantification problem by using DI values for training, and provides confidence bounds on the estimated states. In addition, the proposed method is shown to quantify multiple structural states simultaneously from incoming DI test points. This framework is applied to three test cases: a Carbon Fibre-Reinforced Plastic (CFRP) coupon with attached weights as simulated damage, an aluminum coupon with a notch and an aluminum coupon with attached weights as simulated damage under varying loading states. The novel state prediction method presented herein is applied to single-state quantification in the first two test cases, as well as the third one assuming the loading state is known. Finally, the proposed method is applied to the third test case assuming neither the damage size nor the load is known in order to predict both simultaneously from incoming DI test points. In applying this framework, two forms of GPRMs (standard and variational heteroscedastic) are used in order to critically assess their performance with respect to the three test cases.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-31T12:13:54Z
      DOI: 10.1177/14759217221098715
       
  • Co-located dual-wave ultrasonics for component thickness and temperature
           distribution monitoring

    • Free pre-print version: Loading...

      Authors: Yifeng Zhang, Frederic Cegla
      Abstract: Structural Health Monitoring, Ahead of Print.
      Permanently installed ultrasonic sensors have found increasing applications in the field of structural health monitoring (SHM), in particular with respect to thickness measurement and corrosion monitoring. As ultrasonic velocity is temperature dependent, the state and temperature distribution of a component contribute to much of the measurement uncertainties of an ultrasonic SHM system. On the other hand, the temperature dependency of ultrasonic velocity has also led to various temperature sensing methods for measuring temperature distributions within solid materials. While conventional ultrasound-based techniques can measure either a component’s thickness at a given temperature, or the internal temperature distributions at a given component thickness, measurement fluctuations and drifts can occur if both variables are set to change simultaneously. In this study, we propose a dual-wave approach to overcome the limitations of the existing methods. ‘Co-located’ shear and longitudinal pulse-echo measurements are used to simultaneously track the thickness change and through-thickness temperature variation of a steel plate in complex environmental conditions. Results of the verification experiments showed that, in the given conditions, the proposed dual-wave correction method could reduce thickness measurement uncertainties by approximately a factor of 5 and eliminate 90% of the drift in temperature predictions.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-31T08:39:39Z
      DOI: 10.1177/14759217221104463
       
  • Adaptive Autoregressive Modelling Based Structural Health Monitoring of RC
           Beam-Column Joint Subjected to Shock Loading

    • Free pre-print version: Loading...

      Authors: Anupoju Rajeev, Lavish Pamwani, Shivam Ojha, Amit Shelke
      Abstract: Structural Health Monitoring, Ahead of Print.
      In the present work, a novel technique based on the combination of singular spectral analysis (SSA) and recursive estimate of coefficients of adaptive autoregressive (AR) modelling is employed to identify the damage in the reinforced (RC) beam-column joints. The damage is induced by imparting shock load at the tip of the beam-column joints. The damage is identified with the help of the acceleration response of the healthy and damaged specimens excited by high intensity white noise. The proposed approach has two major components, first, filtering and removing the noise from the dynamic response using the singular spectral analysis and second, modelling the filtered response using adaptive AR process to get the recursive estimate of coefficient matrix for baseline and damage states. The coefficients evaluated for each time instant are presented in a multi-dimensional subspace to form distinct clusters corresponding to a healthy and damaged state. In order to identify and quantify the damage, the geometrical and statistical measures are evaluated that quantifies the segregation of clusters. In total, three distinct measures are used to quantify the damage, namely, Euclidean distance (ED), Mahalanobis distance (MD) and Bhattacharyya distance (BD). The BD accounts the variation in the distribution of both the clusters, thereby shows superior results comparatively than ED and MD. The results of DSFs also manifest the superiority of BD over the other two DSFs. These geometrical and statistical distances are the damage sensitive feature (DSF) to identify and quantify the damage in the specimen due to the shock load. The obtained results of all the DSFs show good consistency with the maximum deformation of the specimen due to shock loading highlighting the accuracy of the proposed algorithm.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-30T12:43:17Z
      DOI: 10.1177/14759217221101325
       
  • An MPPCA-based approach for anomaly detection of structures under multiple
           operational conditions and missing data

    • Free pre-print version: Loading...

      Authors: Zhi Ma, Yaozhi Luo, Chung-Bang Yun, Hua-Ping Wan, Yanbin Shen
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural anomaly detection based on the structural health monitoring (SHM) data has attracted significant attention owing to its important role in the early warning of structural damage to existing civil structures. Data-driven approaches, where damage-sensitive features are extracted directly from the SHM data using statistical pattern recognition (SPR) techniques without physical models of structures, have been widely studied. Principal component analysis (PCA) and probabilistic PCA (PPCA) are powerful and efficient SPR methods for linear or weakly nonlinear cases. However, some special structures may be subjected to multiple operational conditions, wherein structural configurations such as geometry and mass distribution may change due to the movement of parts or the whole structure, as in retractable roof structures. These changes may give erroneous results in the SPR of the SHM data and eventually in the anomaly detection by a single PCA or PPCA model. This paper presents an improved approach using a mixture of probabilistic principal component analysis (MPPCA) for the anomaly detection of structures under multiple operational conditions with missing measurement data. First, the baseline MPPCA model was constructed for stress data collected under healthy conditions, where the estimation of the MPPCA parameters was reformulated for the missing data cases. Second, three anomaly statistics were presented for newly monitored incomplete data to detect and localize structural anomalies. The probability distributions of the anomaly statistics were estimated to obtain thresholds for outlier detection. Finally, the effectiveness of the MPPCA-based method was investigated by applying the method to the anomaly detection of a retractable roof structure with numerically simulated and real monitored stress data.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-30T10:53:25Z
      DOI: 10.1177/14759217221100708
       
  • Enhancing damage localisation in critical areas of storage tanks in
           floating production storage and offloadings with a guided wave structural
           health monitoring system

    • Free pre-print version: Loading...

      Authors: Paulo Dambros Menin, Lúcio de Abreu Corrêa, Thomas Gabriel Rosauro Clarke, Denis Alvin Liang
      Abstract: Structural Health Monitoring, Ahead of Print.
      A guided wave monitoring system for storage tanks of floating production storage and offloading units was tested on a panel which is representative of a critical region of such structures. Initially, baseline signals of the undamaged structure were acquired within a representative temperature range. These signals were then processed with the optimal baseline subtraction, optimal stretch and phase-shift compensation algorithms. Two distinct damage indexes were generated from the residual signals in order to verify the detection capability of the system. A delay-and-sum image algorithm was then applied to the residual signals in order to evaluate the defect localisation capabilities of the system. These images were than post-processed with three different methods, one based on a probabilistic approach, another on singular value decomposition and also with a combination of these two methods. The signal-to-noise ratio (SNR) gain obtained by applying the post-processing strategies was calculated and compared to the original images. Results demonstrate that the system is capable of detecting localised wall loss and indicating its correct position for damage larger than 20% wall thickness loss. Results also show that the post-processing methods helped to improve the SNR, leading to gains of more than 15dB in damage to artefact amplitude ratios.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-30T10:51:37Z
      DOI: 10.1177/14759217211066558
       
  • Damage quantification using transfer component analysis combined with
           Gaussian process regression

    • Free pre-print version: Loading...

      Authors: Marcus Omori Yano, Samuel da Silva, Eloi Figueiredo, Luis G Giacon Villani
      Abstract: Structural Health Monitoring, Ahead of Print.
      Machine learning methods used in Structural Health Monitoring applications still have generalization difficulties among structures, even when structures are nominally and topologically similar. The data sets present divergences between their probability distributions that do not allow the model’s generalization for damage detection. This issue is even more complex in situations where one wants to quantify damage levels through data sets collected from different structures. Transfer learning methods offer a solution to overcome those limitations, using relevant information from a labeled structure (source domain) to assist the analysis of another structure (target domain) under unknown conditions. Therefore, this paper proposes the use of transfer component analysis to mitigate divergences between the model/structure’s features, and the label consistency requirement is applied in combination with a Gaussian process regression model for damage quantification. The effectiveness of the estimated model improves when the labels consistency between domains is achieved, indicating the current damage level in the structure when the regression model achieves its best performance (lowest error). The proposed methodology is applied on the benchmark data of a three-story building structure from the Los Alamos National Laboratory using the knowledge from its numerical model under several conditions, where the complete information of its behavior is available. The results compare the analysis in the original space and after applying the proposed methodology, demonstrating an improvement of the performance in the damage detection and quantification steps.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-28T03:35:31Z
      DOI: 10.1177/14759217221094500
       
  • A Machine learning-based approach to determining stress in rails

    • Free pre-print version: Loading...

      Authors: Matthew Belding, Alireza Enshaeian, Piervincenzo Rizzo
      Abstract: Structural Health Monitoring, Ahead of Print.
      Recent advancements in both software and hardware have sparked the use of machine learning (ML) in structural health monitoring (SHM) applications. This paper delves into the use of ML to determine axial stress in continuous welded rails (CWR). The overall proposed SHM strategy consists of monitoring the vibration of CWR and associating their modal characteristics to the rail longitudinal stress using a ML algorithm trained with data generated with a finite element model. In the present study, the feasibility of the proposed strategy was tested on a simple rail segment subjected to mechanical compression. Two algorithms were developed using hyperparameter search optimization techniques to infer the stress from the frequencies of vibration of a few modes of the rail. The training data were generated with a finite element model of a rail segment under varying axial stresses, rail lengths, and boundary conditions at the two ends of the segment. The algorithms were then tested with a second set of data generated numerically and the results of an experiment in which a 2.4-m-long rail was subjected to compressive load and excited with an instrumented hammer. Both tests demonstrated that ML is a viable tool to estimate axial stress in the rail segment provided a sufficient number of modes of vibrations are presented to the learning algorithm. For the future, more experiments are warranted to test the ML against data from real CWR.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-27T12:50:06Z
      DOI: 10.1177/14759217221085658
       
  • Environmental impact assessment of guided wave-based structural health
           monitoring

    • Free pre-print version: Loading...

      Authors: Clarisse Aujoux, Olivier Mesnil
      Abstract: Structural Health Monitoring, Ahead of Print.
      Guided wave-based structural health monitoring (GW-SHM) relies on permanently installed sensors to detect and monitor structural defects such as cracks or delamination. Millimeter to centimeter-sized defects are detected over areas of several square meters with sparse sensor networks installed on metallic or composite structures. So far, the main drivers of the development of such technologies have been expectations of cost savings and/or an increased safety. As monitoring requires a complex cyber physical system including, at least an energy source, sensors, data acquisition, treatment, and communication capabilities, adding monitoring capabilities to a structure leads to an intrinsic environmental impact. This paper targets for the first time the environmental assessment of GW-SHM. Such analysis being use-case dependent, two prospective applications are studied: railroad monitoring and wind turbine monitoring. A complete GW-SHM system prototype is defined to quantify its environmental impact, and exploitation scenarios are proposed to estimate the potential environmental gains provided by the monitoring. For the scenarios under consideration, the studied system is found to be either carbon neutral or carbon negative (i.e., favorable), but this result is limited to the functional units under consideration. The present study is expected to be a template for further environmental assessments of monitoring systems, which shall be conducted for each prospective application and refined often as systems mature and exploitation scenarios become clearer and exploitation data available.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-27T12:27:43Z
      DOI: 10.1177/14759217221088774
       
  • Multi-index probabilistic anomaly detection for large span bridges using
           Bayesian estimation and evidential reasoning

    • Free pre-print version: Loading...

      Authors: Xiang Xu, Michael C Forde, Yuan Ren, Qiao Huang, Bin Liu
      Abstract: Structural Health Monitoring, Ahead of Print.
      To measure uncertainties within anomaly detection and distinguish sensor faults from anomalous events, a multi-index probabilistic anomaly detection approach is proposed for large span bridges based on Bayesian estimation and evidential reasoning. To avoid false detection raised by signal spikes, an energy index is first defined and extracted from pre-processed measurements, including missing data recovery and thermal response separation. Then, a probabilistic index, namely, certainty degree, is derived from probability density functions of detection triggers – extreme values predicted by using Bayesian estimation of the generalized Pareto distribution. To distinguish sensor faults from anomalous scenarios, evidential reasoning is applied to incorporate multiple certainty degrees into a joint one under the assumption that the probability of multi-sensor failing simultaneously is extremely low. Specifically, a large joint certainty degree indicates a high occurrence probability of anomalous scenarios, while a small one together with a large individual certainty degree depicts a high probability of sensor faults. Finally, the effectiveness of the proposed anomaly detection method is validated through structural health monitoring data from the Nanjing Dashengguan Yangtze River Bridge. Measurements from four sensors, that is, three cable forces and one deflection, are selected to detect anomalies based on their high pair-wise correlations. Two case studies are presented, namely, sensor fault detection and snow disaster detection. The sensor fault is detected through a certainty degree of almost 100% for the individual index and a joint certainty degree of nearly 0. The snowstorm is detected by a joint certainty degree of 36.82%.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-27T11:59:17Z
      DOI: 10.1177/14759217221092786
       
  • Deep learning-based indirect bridge damage identification system

    • Free pre-print version: Loading...

      Authors: Donya Hajializadeh
      Abstract: Structural Health Monitoring, Ahead of Print.
      With the growing number of well-aged bridges and the urgency in developing reliable, (pseudo-) real-time monitoring of structural safety and integrity, there is a worldwide and widespread campaign toward transforming structural health monitoring practice. Among these attempts, the application of data-driven approaches in developing damage identification techniques has received particular attention in recent years. Given the growing volume of structural health monitoring data, the power of data-driven approaches has been further exploited. These efforts have been predominantly focused on building and training algorithms using direct measurements from bridges. Although recent years have seen transformative technologies in producing cheap and wireless sensors, network-wide bridge instrumentation is logistically difficult and expensive. This has led to a new group of structural health monitoring systems entitled indirect or drive-by approaches. In drive-by systems, measurements from an instrumented vehicle are used to extract structural damage signatures. In other words, in these systems, the instrumented vehicle acts as both actuator and receiver while passing over a bridge. The main challenge in deploying drive-by approaches for damage identification purposes is that the signals collected on drive-by vehicles also embody signatures from the vehicle, road/rail profile and are easily contaminated by environmental and operational conditions. Furthermore, the majority of current drive-by damage identification systems rely on prior knowledge of vehicle or bridge dynamic characteristics which has led to limited application of the concept in practice so far. To address these challenges, this study employs a powerful class of deep learning algorithm to develop a damage identification system using measurements on an instrumented travelling train. The proposed algorithm is capable of automatically extracting damage signatures from train-borne measurements only. To demonstrate the algorithm’s capability, the method is applied to measurements collected on a model instrumented train travelling on a simply supported model steel bridge. For this purpose, a deep convolutional neural network is built, optimised, trained and tested to detect damage using acceleration signals collected on the instrumented train only. The hyperparameters of the algorithm are optimised using the Bayesian optimisation technique. The accuracy of the algorithm is experimentally tested for four positive damage scenarios (combination of two different locations and intensity) and three different travelling speeds. This is the first demonstration of the data-driven drive-by damage identification system under scaled operational environment conditions. The performance of the proposed method is discussed under different travelling speeds and different damage states. The result shows that the proposed method can accurately and automatically detect and classify damage under varying speed, rail irregularities and operational noise using train-borne measurements only and offers a great promise in transforming the future of bridge damage identification system.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-27T11:52:48Z
      DOI: 10.1177/14759217221087147
       
  • A novel spectral coherence-based envelope spectrum for railway axle-box
           bearing damage identification

    • Free pre-print version: Loading...

      Authors: Bingyan Chen, Dongli Song, Weihua Zhang, Yao Cheng
      Abstract: Structural Health Monitoring, Ahead of Print.
      Damage identification of axle-box bearings is essential to ensure the safe operation of railway trains. The envelope spectrums generated by spectral coherence are effective bearing damage identification tools, but the traditional spectral coherence-based envelope spectrums cannot effectively reveal the bearing damage features under strong interference noise or cannot fully extract the damage information distributed in multiple spectral frequency bands. To solve these problems, a weighted combined envelope spectrum (WCES) based on spectral coherence is proposed as an enhanced bearing damage detector in this paper. First, the frequency domain signal-to-noise ratio (FDSNR) is devised to measure the damage information in each spectral frequency component of spectral coherence. Then, an information threshold is introduced into the estimated FDSNR to construct a weighting function to enhance the informative spectral frequency components and eliminate the interference components. Eventually, the spectral coherence normalized by the weighting function is integrated to generate WCES for bearing damage identification. The simulation and experimental results indicate that the proposed method can effectively excavate the fault-related information and detect railway axle-box bearing damages, and the comparisons with the state-of-the-art methods demonstrate the superiority of the proposed method.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-27T05:57:39Z
      DOI: 10.1177/14759217221095067
       
  • Locating of acoustic emission source for stiffened plates based on
           stepwise time-reversal processing with time-domain spectral finite element
           simulation

    • Free pre-print version: Loading...

      Authors: Zexing Yu, Jiaying Sun, Chao Xu, Fei Du
      Abstract: Structural Health Monitoring, Ahead of Print.
      Stiffened thin-walled structures are widely utilized in aerospace engineering as critical load-bearing components. These structures are prone to be damaged from external impact or corrosion, and fatigue cracks. Acoustic emission (AE) is a key phenomenon accompanying damage and can be used as an efficient indicator to locate the damage in stiffened structures. In this paper, a novel AE source locating approach is proposed, which is based on combining the concepts of the time-reversal (T-R) guided wave and time-domain spectral finite element method (T-D SFEM), in which an improved T-R strategy named stepwise T-R is developed to overcome the defocus issue, and the T-D SFEM is utilized to simulate the re-emitted wavefield. Benefit from the improvement and virtual simulation, the focused wavefield can be rapidly obtained without high-cost and large bulk wavefield imagine devices. The approach is validated experimentally. In addition, the effects of the signal length, mesh size, and noise levels on locating are studied in different scenarios. The results show that the proposed approach can locate the AE source in the stiffened plate efficiently and accurately. The optimal signal length and suggested mesh size are also decided. Besides, the robustness of the proposed method is demonstrated and the results are effective in the case of the measured signals with 30% white noise.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-27T05:42:50Z
      DOI: 10.1177/14759217221094462
       
  • Study on cage slip for rolling bearing under speed variation conditions

    • Free pre-print version: Loading...

      Authors: Liwei Zhan, ZhengHui Li, Shi Zhuo, Ping Gong, Jingyan Luan, Xu Feng, Chengwei Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      The rolling bearing failure from cage slip under fast speed variation is an important issue that has drawn a wide interest. To ensure the safe operation, cage slip needs to be detected. However, due to the limit of bearing working environment of high temperature, oil mist, and small mounting space, the conventional methods such as optical fiber, ultrasound, and eddy current detection are not always feasible. This paper presents a novel method to detect cage slip based on weak magnetic detection under the speed variation. The rolling bearing, which is in the earth magnetic field, can be magnetized weakly. By the detection of the weak magnetized bearing on the earth magnetic field disturbance, the roller speed information can be obtained. This method is not affected by the bearing work environment. Then, the signal processing technology based on the ridge detection is proposed. By the speed spectrum of inner raceway, the searched path can be re-grouped. And the searched boundary is determined by assuming the roller pure rolling. Combining the searched path and the boundary, the local neighborhood of the roller rotational instantaneous frequency (IF) is determined. By finding the magnitude maximum of the local neighborhood, the roller IF can be extracted. Finally, the time-varying spectrum of the cage slip can be evaluated under the speed variation. By the comparison with the traditional optical method, it illustrates that the cage slip goes through dramatic changes under speed variation and is almost unchanged under the stabilization stage.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-27T05:37:07Z
      DOI: 10.1177/14759217221089852
       
  • A novel adaptive boosting algorithm with distance-based weighted least
           square support vector machine and filter factor for carbon fiber
           reinforced polymer multi-damage classification

    • Free pre-print version: Loading...

      Authors: Wenjuan Sheng, Yutao Liu, Dirk Söffker
      Abstract: Structural Health Monitoring, Ahead of Print.
      Adaptive boosting (AdaBoost) algorithms fuse multiple weak classifiers to generate a strong classifier by adaptively determining the fusion weights of the weak classifiers. According to incorrect or correct classification results, sample weights become larger or smaller. However, this weight update scheme neglects valuable information in the results. Moreover, an important requirement for weak classifiers is an accuracy higher than random guessing. This requirement is likely to lead to an unexpected result. This means that several generated weak classifiers with similar classification results cannot learn from each other. Consequently, the advantage of fusing multiple weak classifiers disappears. The classification and therefore distinction of different failure modes in materials is a typical task for classical nondestructive testing approaches as well as for new approaches based on machine learning methods. In the case different approaches can be applied, the main question is, which and how tuned approaches provide the best results in terms of accuracy or similar metrics. In the multi-damage classification task distinguishing different physical failure mechanisms in Carbon Fiber Reinforced Polymer (CFRP), the above two aspects complicate the application of AdaBoost algorithms. To improve the results, a novel AdaBoost with distance-based weighted least square support vector machine (WLSSVM) and filter factor is proposed. The distance-based WLSSVM is employed to increase the diversity of weak classifiers, the distances of the classification plane and samples are used to measure the classification task. The filter factor is proposed to filter out unnecessary classifiers contributing less to the final decision. The experimental results demonstrate that the improved AdaBoost schemes with distance-based WLSSVM and filter factor outperform state-of-the-art algorithms. The effects of the scheme using the new weighted update and the filter factor on the algorithm are discussed, respectively. The experimental results show that the combination of the two schemes perform better than other schemes.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-27T04:51:34Z
      DOI: 10.1177/14759217221098173
       
  • An up-scaling temperature compensation framework for guided wave–based
           structural health monitoring in large composite structures

    • Free pre-print version: Loading...

      Authors: Ilias N. Giannakeas, Zahra. Sharif Khodaei, M. H. Aliabadi
      Abstract: Structural Health Monitoring, Ahead of Print.
      Variations in environmental conditions can significantly impair the accuracy and reliability of guided wave structural health monitoring systems. Acquisition of baseline signals over a wide temperature range for the purposes of damage detection and localization is impractical for large composite structures. A novel framework for compensating the effect of temperature at a post-processing stage is presented in this paper to allow updating the compensation factors using observations obtained at different scales. The proposed methodology utilizes observations collected at the lower scales, where a large amount of data under controlled environment is available. Subsequently, the estimated compensation factors are propagated to the higher scales as priors within a Bayesian framework. This way, the measurements required from the high levels are reduced while making it possible to also update the estimated factors during the operation of the structure. The performance of the methodology is evaluated at different scales and compared with the direct use of compensation factors obtained from coupon studies only. It is demonstrated that the proposed methodology improves the fidelity of the compensation algorithm leading to a reduction in the uncertainty of the temperature-compensated signals. Based on the findings of the present study, the reduction in the uncertainty of the compensation improves the performance of both damage detection as well as damage localization in a large composite panel.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-26T12:59:38Z
      DOI: 10.1177/14759217221095415
       
  • Robust correlation mapping of train-induced stresses for high-speed
           railway bridge using convolutional denoising autoencoder

    • Free pre-print version: Loading...

      Authors: Jin Niu, Zhonglong Li, Yi Zhuo, Hao Di, Jianfeng Wei, Xin Wang, Yapeng Guo, Shunlong Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      Train-induced stresses in different monitoring points not only reflects local mechanical characteristics of the structural components but also has inherent spatiotemporal correlations in the high-speed railway bridges. Mapping correlations among the stress responses can assist recognizing train-induced stress pattern and lay foundation for structural health diagnosis. However, correlation mapping and feature extraction of structural responses rely heavily on the data integrity, the pre-constructed model may be out of action due to data acquisition/transmission error, sensor faults, etc., commonly exists in the structural health monitoring system. Considering the characteristics of various incomplete train-induced stresses, this study presents a robust correlation mapping of incomplete data to complete data using one-dimensional convolutional denoising autoencoder. Stacked convolutional layers are employed as encoder to extract robust spatiotemporal feature of incomplete stresses, and transposed convolutional layers served as decoder to reconstruct denoised and complete stresses. In the training strategy, various incomplete data conditions, where stress data are lost continuously or discretely with different missing rates, are considered as training samples, making the established correlation mapping robust, accurate, and adaptive. The application on a high-speed railway truss bridge demonstrates that the proposed method can robustly reconstruct the complete stress data under different data loss conditions. The method can also be employed to assess the importance of any sensor combinations to the monitoring item, which shed light on the maintenance of sensor network.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-26T06:45:44Z
      DOI: 10.1177/14759217221095191
       
  • Heterogeneous structural responses recovery based on multi-modal deep
           learning

    • Free pre-print version: Loading...

      Authors: Bowen Du, Liyu Wu, Leilei Sun, Fei Xu, Linchao Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      For structural health monitoring, a complete dataset is important for further analysis such as modal identification and risk early warning. Unfortunately, the missing data normally exist in current database due to sensor failures, transmission system interruption, and hardware malfunctions. Currently, most of the studies just deleted the dataset containing missing data or using mean values as imputation which could wrongly reflect the characteristics changes of the structure. The present study therefore develops a heterogeneous structural response recovery method based on multi-modal fusion auto-encoder which can consider temporal correlations and spatial correlations and correlations between heterogeneous structural responses simultaneously. Moreover, a parallel optimization method is proposed to optimize the parameters of the deep fusion networks. A dataset containing about 3 months and two input attributes is collected from a bridge and utilized for training and testing the proposed method and some benchmark methods. Statistical scores including root mean square error (RSME), mean absolute error (MAE), and mean relative error (MRE) are applied to evaluate the performance of the implemented models, respectively. Results show that the proposed method achieve the best imputation performance under different missing scenarios. Furthermore, the proposed method can achieve better performance when the missing rate is high. The results suggest that the consideration between heterogeneous structural responses is critical for missing data recovery.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-26T01:50:22Z
      DOI: 10.1177/14759217221094499
       
  • Laboratory and field experiment validations on the use of hydraulic
           transients for estimating buried water pipeline deterioration

    • Free pre-print version: Loading...

      Authors: Jane Alexander, Zhao Li, Pedro Lee, Colin Roxburgh, Mark Davidson
      Abstract: Structural Health Monitoring, Ahead of Print.
      This paper investigates the validity of using fluid transients as a rapid screening tool to augment existing methods for assessing the condition of buried water systems. In particular, the paper provides one of the first detailed investigations on the impact of pipe wall deterioration on the characteristic of transient waves in both laboratory and field experiments. Laboratory pipeline sections were deteriorated by an accelerated corrosion process using controlled electrolytic cell reactions, and three cases were considered: where corrosion is limited to the internal wall, limited to the external wall, and where both internal and external walls are corroded. Hydraulic transient tests were carried out to measure the transient wave speed in these corroded pipe sections, and the measurements are found to be consistent with the theoretical predictions using the observed wall thickness loss, confirming that wave speed can be used as an indicator of pipe wall deterioration. Field experiments were carried out in the water supply network in the Waimakariri District, New Zealand, to validate the performance of this pipe condition diagnostic methodology. Transient tests were conducted on selected 60-year-old sections of asbestos cement (AC) pipelines and sensors were connected to standard fire hydrants to measure the wave speeds and wave reflections. Compared with the theoretical calculated wave speed of intact pipelines, a 140–300 m/s wave speed decrease was observed in most of the tested sections which is in line with the expectation for pipelines of this age. The wave speeds were used to predict the in-situ thickness of the pipe wall and these predictions were found to match with computed tomography X-ray scan results of the excavated pipe with good accuracy. The same methodology was also used to correctly detect an unrecorded plastic pipe section in the AC pipe network.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-26T01:49:02Z
      DOI: 10.1177/14759217221093666
       
  • Tunnel lining water leakage image Segmentation based on improved BlendMask

    • Free pre-print version: Loading...

      Authors: Peng Geng, Mengye Jia, Xiaoxia Ren
      Abstract: Structural Health Monitoring, Ahead of Print.
      Water leakage of tunnel is a common disease in all kinds of existing tunnels. Automatic, timely, and accurate detection of water leakages is of great significance to the safe operation and maintenance for all kinds of tunnels. Due to the complicated background of tunnel surfaces, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. To address these problems, this article presents an improved BlendMask image segmentation model with top-down and bottom-up architecture which accurately extract tunnel’s water leakage area from tunnel lining images. The proposed method is validated by an experimental study, and the results are compared with those obtained by the tunnel defect segmentation methods including Mask R-CNN and DeepCrack and other deep learning methods including CondInst, SoloV2, fully convolutional network and UNet. The Recall, Precision, F1-score, Dice and mean intersection over union (mIoU) for the proposed method are superior than those by the other methods above with respect to on test images. The subjective analysis on predicted results by different methods also shows the presented method can perform well.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-26T01:45:05Z
      DOI: 10.1177/14759217221093568
       
  • An integrated condition monitoring scheme for health state identification
           of a multi-stage gearbox through Hurst exponent estimates

    • Free pre-print version: Loading...

      Authors: Vamsi Inturi, Sai Venkatesh Balaji, Praharshitha Gyanam, Brahmini Priya Venkata Pragada, Sabareesh Geetha Rajasekharan, Vikram Pakrashi
      Abstract: Structural Health Monitoring, Ahead of Print.
      The vibration and acoustic signals collected from rotating machinery are often non-stationary and aperiodic, and it is a challenge to post-process and extract the defect sensitive health indicators. In this paper, we demonstrate how the estimated Hurst exponent of such measured data can be advantageous for analyzing non-stationary and aperiodic data due to its self-similarity and scale-invariance properties. To illustrate this, the paper demonstrates detection of fault diagnostics of a multi-stage gearbox operating under fluctuating speeds through estimated Hurst exponent of the raw vibration and acoustic signals as a health indicator. Thirteen health states of the gearbox are considered, and the raw vibration and acoustic signals are collected. The Hurst exponents are calculated using three different approaches: generalized Hurst exponent (q = 1, 2, 3, and 4), rescale range statistical (R/S) analysis, and dispersion analysis from the vibration and acoustic signals. Three different health indicator datasets are formulated and subjected to feature learning through conventional machine-learning (decision tree and support vector machine) and advanced machine-learning (deep-learning) classifiers. The effectiveness of these datasets while discriminating between the health states of the gearbox is investigated, yielding classification accuracies of 96.4% when compared with the individual health indicator datasets. The ability of the fault diagnosis and defect severity analysis with reduced reliance on the signal post-processing algorithms is demonstrated.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-26T01:44:51Z
      DOI: 10.1177/14759217221092828
       
  • Robust fault diagnosis of rolling bearing via phase space reconstruction
           of intrinsic mode functions and neural network under various operating
           conditions

    • Free pre-print version: Loading...

      Authors: Rui Yuan, Yong Lv, Zhiwen Lu, Si Li, Hewenxuan Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      Rolling bearings are important components in mechanical, civil, and aerospace engineering. The practical working conditions of rolling bearings are complex; hence, fault diagnosis of rolling bearings under various operating conditions is very challenging. This paper proposes a novel approach to fault diagnosis of rotary machinery using phase space reconstruction (PSR) of intrinsic mode functions (IMFs) and neural network under various operating conditions. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to decompose vibration signal of rotary component into IMFs denoting high-to-low instantaneous frequencies adaptively. PSR constructs one-dimensional IMFs to high-dimensional IMFs, which helps reveal the underlying nonlinear geometric topology via the reconstructed inherent and hidden dynamical characteristics of the one-dimensional vibration signal. To explore intrinsic dynamical properties, interquartile range (IQR) of Euclidean distance (ED) values of high-dimensional IMFs are extracted as condition indicators and used as input of back propagation (BP) neural network to fulfill fault identification of rolling bearings. The effectiveness and superiority of the proposed approach have been validated by theoretical derivations, numerical simulations and experimental data. The results show that the proposed approach is promising in fault diagnosis of rotary machinery under various operating conditions.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-26T01:29:29Z
      DOI: 10.1177/14759217221091131
       
  • A novel method for steel bar all-stage pitting corrosion monitoring using
           the feature-level fusion of ultrasonic direct waves and coda waves

    • Free pre-print version: Loading...

      Authors: Xiangtao Sun, Minghui Zhang, Weihang Gao, Chuanrui Guo, Qingzhao Kong
      Abstract: Structural Health Monitoring, Ahead of Print.
      Corrosion monitoring of steel bars has drawn extensive attention in recent decades. Conventional ultrasonic method, utilizing direct waves to detect damage, is adequate for severe pitting corrosion but suffers from low sensitivity to incipient pitting corrosion. Coda wave technique, a very sensitive method to subtle changes in medium using later arrival wave packets, is innovatively introduced to monitor pitting corrosion of steel bars, especially in the early stages. The decorrelation coefficient (DC) values are calculated to quantify the variations of both direct waves and coda waves. To overcome the limitations of coda waves for severe pitting corrosion and remedy the low sensitivity of direct waves for incipient pitting corrosion, a feature-level data fusion strategy is proposed to integrate the two probing waves to monitor all-stage pitting corrosion of steel bars. The combination of direct waves and coda waves could exploit the complementary merits in various pitting corrosion configurations. The proposed feature-level fusion strategy of ultrasonic coda waves and direct waves intercepted from the same recorded signals opens a new perspective in all-stage pitting corrosion monitoring of steel bars and contributes a novel scheme for whole-process damage evaluation of structures.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-25T12:55:01Z
      DOI: 10.1177/14759217221094466
       
  • Comparative study of stator current-based and vibration-based methods for
           railway traction motor bearing cage fault diagnosis at high-speed
           condition

    • Free pre-print version: Loading...

      Authors: Qi Sun, Chunjun Chen, Xinchang Liu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Traction motor’s rolling element bearings are one of the most critical components for the structural health monitoring of high-speed train vehicles. Most structural health monitoring techniques for railway rolling element bearings are using vibration signal. However, the vibration characteristic of cage defect is difficult to be transmitted to external monitoring point owing to the location of cage in bearing. Hence, the cage defect of Railway Traction Motor Bearing (RTMB) is usually difficult to be recognized with motor housing vibration acceleration signal. Considering Induction Motor (IM) itself a sensor, the mechanical imbalance of bearing cage defect can reflect in stator current through the motor gap magnetic field rather than a solid transmission path. A novel strategy with stator current for monitoring the traction motor bearing cage health status in High-Speed Train (HST) is proposed in this paper. This method requires no additional sensor in the practical application. To prove the strategy is feasible and more effective than vibration-based method for the cage fault diagnosis of RTMB, a comprehensive experiment investigation with a real railway traction motor of a HST is completed. Artificial cage defects are processed on bearings at both ends of the induction motor, respectively. Three high-speed (≥250 km/h) conditions are observed. The data analysis results show that vibration-based method is almost invalid at high-speed condition and the proposed current-based method is feasible and effective for the fault diagnosis of high-speed railway traction motor bearing cage.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-25T11:41:51Z
      DOI: 10.1177/14759217221104217
       
  • Stereo-vision-based 3D concrete crack detection using adversarial learning
           with balanced ensemble discriminator networks

    • Free pre-print version: Loading...

      Authors: Seungbo Shim, Jin Kim, Gye-Chun Cho, Seong-Won Lee
      Abstract: Structural Health Monitoring, Ahead of Print.
      The functional performance of concrete structures degrades over time as a result of continuous loads, stress fatigue, and external environmental changes. Thus, periodic diagnoses and inspections are essential because such conditions can eventually lead to disaster. Hence, the detection of cracks in concrete is a key component of structural management. In recent years, deep-learning-based computer vision technologies have emerged as a promising trend and have been actively used for crack detection. Unfortunately, the performance of existing crack detection technologies decreases under environmental conditions that vary widely. To resolve this issue, we propose a new deep neural network that applies an optimal mixing ratio of training data to improve recognition performance alongside an adversarial learning-based balanced ensemble discriminator network. Furthermore, a method to reconstruct the 3-dimensional shape of cracks is proposed using a stereo-vision-based triangulation measurement technique that determines the size of detected cracks. Experimental results show that the proposed algorithm achieved a crack detection performance with a mean intersection-over-union of 84.53% and an F1 score of 82.91%. The proposed inspection technology for concrete structures is expected to be implemented in the future in connection with various automation techniques.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-25T11:31:25Z
      DOI: 10.1177/14759217221097868
       
  • Prediction of frequency and spatially dependent attenuation of guided
           waves propagating in mounted and unmounted A380 parts made up of
           anisotropic viscoelastic composite laminates

    • Free pre-print version: Loading...

      Authors: Shuanglin Guo, Marc Rébillat, Nazih Mechbal
      Abstract: Structural Health Monitoring, Ahead of Print.
      Monitoring damage in composite structures using guided wave-based techniques is particularly effective due to their excellent ability to propagate over relatively long distance and hence to cover a large area with few testing time and equipment. The industrialization of this method is highly tributary of the number and placement of the active elements. Yet, the optimal sensorization of a structure relies on the decrease in amplitude of guided waves over propagation distance. A reliable prediction of attenuation of guided waves is still a challenge especially for anisotropic viscoelastic composite materials which exhibit complex changes of attenuation with propagation direction and thus a spatial dependency of attenuation. In this paper, the damped global matrix method (dGMM), having stable and efficient merits, is developed to predict the frequency and spatially dependent attenuation of waves propagating in anisotropic composite materials. dGMM integrates three damping models (Hysteretic, Kelvin-Voigt, and Biot models) into the conventional undamped GMM to consider viscoelasticity of composite laminates. The proposed dGMM is first theoretically validated by numerical comparison with the semi-analytical finite element method. In addition, two industrial case studies, parts of an A380 nacelle at scale one, are employed to experimentally validate the proposed attenuation prediction method. The first one is a fan cowl structure and the second one is an inner fixed structure, both either unmounted or mounted on an actual instrumented A380 plane. This makes the validation extremely valuable for both the scientific and industrial communities. The proposed attenuation prediction method thus paves the way to optimally deploy sensor network for structural health monitoring of anisotropic viscoelastic composite structures.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-25T08:58:37Z
      DOI: 10.1177/14759217221099967
       
  • A novel adaptive bandwidth selection method for Vold–Kalman filtering
           and its application in wind turbine planetary gearbox diagnostics

    • Free pre-print version: Loading...

      Authors: Ke Feng, JC Ji, Qing Ni
      Abstract: Structural Health Monitoring, Ahead of Print.
      The planetary gearbox transmission system in wind turbines has complex structures and generally operates under non-stationary conditions. Thus its measured responses are of high complexity and nonlinearity, which brings a great challenge in the development of reliable condition monitoring techniques for the planetary gearbox transmission system. As a prevalent and effective tool for analyzing the non-stationary vibration signal with strong nonlinearity, the Vold–Kalman filtration technique has excellent capabilities of tracking the targeted harmonic components of vibrations, which can significantly benefit planetary gearbox fault diagnostics. However, the tracking accuracy is heavily enslaved to the selection of the rational bandwidth for the Vold–Kalman filter. An inappropriate bandwidth could impair the characteristics of the targeted harmonic responses, and as a consequence, the monitoring process becomes no longer reliable. To address this issue, a novel bandwidth selection methodology for the Vold–Kalman filter is developed in this paper. Through comprehensively depicting the targeted harmonic response using features in multiple domains, the rational bandwidth can be selected for Vold–Kalman filtering, and then, a reliable monitoring process can be ensured. Additionally, a tacho-less speed estimation procedure is utilized in this paper to acquire the instantaneous rotational speed from the vibration signal directly. With the rational bandwidth and the estimated rotational speed, the desired harmonic components of vibrations can be adaptively extracted and tracked through the Vold–Kalman filter with high accuracy, and at the same time, the irrelevant or unwanted components are excluded completely. The effectiveness and superiority of the proposed adaptive Vold–Kalman filtration for wind turbine planetary gearbox diagnostics are demonstrated and validated experimentally.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-24T08:12:46Z
      DOI: 10.1177/14759217221099966
       
  • Synthesis of healthy-structure model responses for damage quantification

    • Free pre-print version: Loading...

      Authors: Hernán Garrido, Martín Domizio, Oscar Curadelli, Daniel Ambrosini
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural Health Monitoring faces several challenges. Among them, especially for the quantification of damage, are (1) the uncertainty in the boundary conditions, (2) the need for a calibrated numerical model, or measurements, of the structure in its healthy state, (3) the variability in the structure properties and boundary conditions due to environmental and operational conditions and (4) the possibility of damage in the virgin structure due to construction defects. Based on the sparsity condition of structural damage, this work presents a method that tackles these challenges simultaneously. The method consists in synthesising the response of a healthy-structure model, which is valid in the current environmental and operational conditions, only inside a region of interest (ROI) that excludes the boundaries and the rest of the full structure. This is accomplished by means of a robust regression of the solution of an analytical model of the healthy structure, and its loading, only using testing data of the (possibly) damaged structure in that ROI. Under ideal conditions, the method showed to be exact in detecting, locating and quantifying damage, in some cases much better than using measurements of the virgin structure. Finally, the method was tested by numerical simulations and using experimental data, under realistic conditions, which evidences its practical applicability.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-24T02:10:16Z
      DOI: 10.1177/14759217221088493
       
  • Distributed detection of internal cavities in concrete-filled steel tube
           arch bridge elements

    • Free pre-print version: Loading...

      Authors: Shilin Gong, Xin Feng, Guanhua Zhang, Farhad Ansari
      Abstract: Structural Health Monitoring, Ahead of Print.
      Internal cavities in structural elements, such as in concrete-filled steel tubes (CFSTs) of arch ribs reduce confinement, bearing capacity, and the durability of the arch bridges. Formation of internal cavities may be either materials related, which affects proper consolidation, or construction related, especially in terms of proper timing and delivery of concrete slurry to horizontal and inclined structural members. Nondestructive test methods such as ultrasonics have been employed for detection of internal cavities. Despite their success, point-by-point inspection of structural members becomes time-consuming and inefficient, especially when entire structures need to be inspected. Infrared thermography (IR) provides a more reasonable global approach for detection of anomalies. The accuracy and resolution of IR depend on the local ambient conditions affecting convection and external thermal flux prior to detection by IR. The research described herein pertains to the development of a hybrid thermographic approach by using Brillouin fiber optic sensors (BFOSs) for direct detection of thermal convection at the surfaces of steel tubes. Accurate quantitative detection of internal cavity locations and their dimensions required development of a specific machine learning-based approach through which the distributed thermographic data was converted to a series of grayscale images. A Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the improved VGG-16 (IVGG-16) network architecture was utilized for this purpose. The experimental work involved design and fabrication of a distributed temperature sensing sheet (DTSS) for simultaneous transmission of thermal energy into CFST specimens and acquisition of distributed thermographic data by the BFOS. By using the proposed approach, it was possible to detect the embedded internal cavities during the experiments.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-24T02:09:55Z
      DOI: 10.1177/14759217221088457
       
  • A method of acoustic emission source location for engine fault based on
           time difference matrix

    • Free pre-print version: Loading...

      Authors: Tong Liu, Cong Han, Qian Lin Wang, Zhen Quan Li, Guoan Yang
      Abstract: Structural Health Monitoring, Ahead of Print.
      The structure of a high-performance engine is becoming more and more complex, so it is very important to accurately and quickly locate the faults to ensure its operation safety. The acoustic emission (AE) signal which is caused by the structural damage of engines contains important structural integrity information; however, the accuracy of existing AE location methods is affected by complex structures, such as stiffeners, holes, variable wall thickness, and interface coupling. Therefore, this paper proposes a new AE source location method that combines the two-step Akaike information criterion (AIC) based on the dispersion curve and the time difference matrix (TDM). This method can precisely locate the faults of complex structures without considering the wave velocity. Through theoretical calculation and numerical simulation, this paper intends to show that the proposed method is superior to the traditional AIC and can track the arrival time of the AE signal more accurately. In addition, experiments on different structures illustrate that the proposed method has higher location accuracy in complex structures. This paper also analyzes the location sensitivity to the number and array of sensors in a complex structure and puts forward an optimal scheme of sensor layout on the condition of high location accuracy. The results show that the proposed method can be used as a reliable tool for AE source location and fault monitoring of complex structures. It will have a wide application prospect in the engine and other fields.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-24T02:09:16Z
      DOI: 10.1177/14759217221088995
       
  • Feature pyramid network with self-guided attention refinement module for
           crack segmentation

    • Free pre-print version: Loading...

      Authors: Jeremy CH Ong, Stephen LH Lau, Mohd-ZP Ismadi, Xin Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Automated pavement crack segmentation is challenging due to the random shape of cracks, complex background textures and the presence of miscellaneous objects. In this paper, we implemented a Self-Guided Attention Refinement module and incorporated it on top of a Feature Pyramid Network (FPN) to model long-range contextual information. The module uses multi-scale features integrated from different layers in the FPN to refine the features at each layer of the FPN using a self-attention mechanism. The module enables the earlier layers and deeper layers of FPN to suppress noise and increase the crack details, respectively. The proposed network obtains an F1 score of 79.43% and 96.19% on the Crack500 and CFD datasets, respectively. Furthermore, the network also generalizes better than other state-of-the-art methods when tested on uncropped Crack500 and field images using the weights trained on CFD. In addition, ablation tests using the Crack500 dataset are conducted. The Self-Guided Attention Refinement module increases the average F1 score and recall by 0.6% and 0.8% while roughly maintaining the average precision. From the ablation test, the inclusion of the Self-Guided Attention Refinement module allows the network to segment finer and more continuous cracks. Then, the module is incorporated on PANet, DeepLab v3+ and U-Net to verify the improvements made to FPN. The results show that the module improves the F1 score, precision and recall compared to the absence of it. Moreover, the Self-Guided Attention Refinement Module is compared with the Self-Adaptive Sparse Transform Module (SASTM). The results show that the Self-Guided Attention Refinement Module provides a more consistent improvement.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-24T02:08:59Z
      DOI: 10.1177/14759217221089571
       
  • Marker-free fatigue crack detection and localization by integrating the
           optical flow and information entropy

    • Free pre-print version: Loading...

      Authors: Cun Xin, Cunjun Wang, Zili Xu, Jun Wang, Song Yan
      Abstract: Structural Health Monitoring, Ahead of Print.
      Fatigue cracks caused by repetitive loads are one of the major threats to the structural integrity of civil infrastructure. Human inspection is the most common method for detecting fatigue cracks, but it is time-consuming, labor-intensive, and unreliable. In this paper, we propose a new vision-based fatigue crack detection and localization method that can detect the fatigue crack with marker-free and high precision using a consumer-grade digital camera. A motion tracking technology called optical flow algorithm is applied to the video for tracking the surface motion of the monitored structure under repetitive load. Then, a crack detection and localization algorithm based on optical flow information entropy are developed to search differential features at different video frames caused by the crack opening and closing. The proposed method’s precision is first validated by doing two experiments and then comparing its precision and efficiency to the existing crack detection methods, including image processing and digital image correlation. The results show that, when compared to the existing vision-based methods, the proposed method can accurately and efficiently identify the fatigue crack even when the crack is surrounded by other crack-like edges, covered by complex surface textures, or invisible to human eyes. In addition, based on the proposed methods, a practical application for calculating the stress intensity factor is given to track crack development.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-23T07:54:09Z
      DOI: 10.1177/14759217221103251
       
  • A hybrid structural health monitoring approach for damage detection in
           steel bridges under simulated environmental conditions using numerical and
           experimental data

    • Free pre-print version: Loading...

      Authors: Bjørn T. Svendsen, Ole Øiseth, Gunnstein T. Frøseth, Anders Rønnquist
      Abstract: Structural Health Monitoring, Ahead of Print.
      This paper presents a novel hybrid structural health monitoring (SHM) framework for damage detection in bridges using numerical and experimental data. The framework is based on the hybrid SHM approach and combines the use of a calibrated numerical finite element (FE) model to generate data from different structural state conditions under varying environmental conditions with a machine learning algorithm in a supervised learning approach. An extensive experimental benchmark study is performed to obtain data from a local and global sensor setup on a real bridge under different structural state conditions, where structural damage is imposed based on a comprehensive investigation of common types of steel bridge damage reported in the literature. The experimental data are subsequently tested on the machine learning model. It is demonstrated that relevant structural damage can be established based on the hybrid SHM framework by separately evaluating different cases considering natural frequencies, mode shapes, and mode shape derivatives. Consequently, the work presented in this study represents a significant contribution toward establishing SHM systems that can be applied to existing steel bridges. The proposed framework is applicable to any bridge structure in which relevant structural damage can be simulated and experimental data obtained.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-21T12:55:54Z
      DOI: 10.1177/14759217221098998
       
  • A Modulation Signal Bispectrum Enhanced Squared Envelope for the detection
           and diagnosis of compound epicyclic gear faults

    • Free pre-print version: Loading...

      Authors: Yuandong Xu, Guojin Feng, Xiaoli Tang, Shixi Yang, Fengshou Gu, Andrew D Ball
      Abstract: Structural Health Monitoring, Ahead of Print.
      Epicyclic gearboxes are prevalent in a variety of important engineering systems such as automotive, aerospace, wind turbines, civil equipment and industrial robots owing to the merits of compact structure and high power density. To ensure the productivities of such important systems, condition monitoring techniques are being actively studied to resolve the challenge of varying transmission paths and multiple modulations in vibration signals acquired from ring gear housing. This paper proposes a new Modulation Signal Bispectrum (MSB) Enhanced Squared Envelope method to analyse the vibration signals acquired by a special On-Rotor Sensing (ORS) transducer that is mounted on the shaft of an epicyclic gearbox. A vibration signal model of ORS measurements from epicyclic gearboxes is presented to show the multiple modulation influences. Moreover, inevitable noise influences are also investigated on the conventional squared envelope and the state-of-the-art spectral correlation analysis. On this base, MSB is introduced to suppress these influences for accurately extracting equally spaced harmonics in the squared envelope and is then integrated to isolate fault signatures, allowing effective fault detection and diagnosis of epicyclic gearboxes, which lead to novel contributions of an MSB Enhanced Squared Envelope (MSB-ESE) approach. An experimental study of compound epicyclic gear faults was conducted to demonstrate the superior performance of MSB-ESE along with the special ORS technique in detecting and diagnosing the compound faults on sun and planet gears.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-21T07:26:30Z
      DOI: 10.1177/14759217221098577
       
  • Monitoring the curing process of in-situ concrete with piezoelectric-based
           techniques – A practical application

    • Free pre-print version: Loading...

      Authors: Zi Sheng Tang, Yee Yan Lim, Scott T. Smith, Ahmed Mostafa, Ah Choi Lam, Chee Kiong Soh
      Abstract: Structural Health Monitoring, Ahead of Print.
      The stiffness and strength properties of freshly poured concrete develop over time as the concrete hardens due to curing. The monitoring of such properties therefore enables timely construction decisions such as formwork removal. Traditional point-in-time destructive tests can be cumbersome, while continuous non-destructive testing is desirable. Piezoelectric-based electromechanical impedance (EMI) and wave propagation (WP) techniques fall into the latter, and they have been verified for monitoring concrete properties during curing in the laboratory. This paper reports the first field application of the EMI and WP techniques for monitoring concrete curing, where smart aggregate (SA) sensors are embedded into concrete pour strips of a multi-storey residential building during construction. For comparison and verification purposes, destructive compression and non-destructive ultrasonic pulse velocity (UPV) tests were conducted. Results obtained from both EMI and WP techniques were consistent and repeatable. They were also comparable to the UPV result, and they showed a close correlation to the compressive strength tests. The current study has also revealed that the electrical signatures acquired from the EMI and WP techniques have a linear relationship. EMI-based and WP-based semi-analytical models (and their derivations) that can quantify the compressive strength and modulus of elasticity of concrete at various curing durations are also presented. This reported study ultimately demonstrates the applicability and practical application of the EMI and WP techniques for real-time measurements, bridging the gap between laboratory-based studies and field applications.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-21T05:12:14Z
      DOI: 10.1177/14759217221087916
       
  • Unsupervised deep learning method for bridge condition assessment based on
           intra-and inter-class probabilistic correlations of quasi-static responses
           

    • Free pre-print version: Loading...

      Authors: Yang Xu, Yadi Tian, Hui Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      Data-driven methods for structural condition assessment have been extensively investigated using deep learning (DL). However, studies on quasi-static response data-based structural health diagnoses are relatively insufficient. The difficulty is that quasi-static response data contain coupled effects of structural parameters and external loads. Considering that the correlation between quasi-static responses subjected to identical external loads is only a function of structural parameters and independent from the external loads, the correlation can therefore be employed as an indicator of the structural condition. This study proposes a condition assessment approach for cable-stayed bridges based on correlation modeling between the deflection of girders and tension in cables. The correlation is modeled by an unsupervised DL network comprising two variational autoencoders (AE) and two generative adversarial networks (GANs). The input and output are marginal probability density functions (PDFs). The DL network is trained as the reconstruction and translation processes to model the intra-class and inter-class correlations. Assumptions of shared latent space and cycle consistency are taken to ensure mutual modeling capacity. The Wasserstein distance between the predicted and ground-truth PDFs of tension in cables is used as an indicator of the structural condition. Using probabilistic correlation of quasi-static responses only requires the PDF of external loads to be identical and does not need the external loads to be precisely identical at any moment, thus relieving time-synchronization restrictions for different sensors. The results show that the predicted PDFs agree well with the ground-truth values under normal conditions. Furthermore, the Wasserstein distance is sensitive to damage and shows noticeable variations when the damage of the stay cable occurs.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-21T04:31:43Z
      DOI: 10.1177/14759217221103016
       
  • An efficient online outlier recognition method of dam monitoring data
           based on improved M-robust regression

    • Free pre-print version: Loading...

      Authors: Zhang Han, Jiankang Chen, Fang Zhang, Zhiliang Gao, Huibao Huang, Yanling Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      Common anomaly recognition methods are easy to misjudge and miss outliers for the online monitoring data. This is a bottleneck problem that needs to be overcome in dam safety management moving toward informatization. Based on the data of nine hydropower stations along Dadu River Basin, this paper analyzed existing problems of the common anomaly identification method and an algorithm was proposed based on improved M-robust regression recognition. In this algorithm, the AR factor was introduced to avoid the defect that the traditional model cannot simulate random variables. The extreme value method and robust estimation were utilized to avoid the leverage effect. The model collapse caused by maximum measured value was avoided through improving the residual calculation model of M-robust and optimizing the weight distribution function. The maximum of the three values, residual quartile difference, discrete quartile difference, and measurement accuracy, was used as an anomaly recognition criterion to improve the evaluation criteria. The algorithm compiled was used in the Dadu River Company since 2017. The statistics showed that for the 150,000 measured values per day, the evaluation time could be within 15 min, the missed judgment rate was 0%, and the misjudgment rate was less than 2%. The proposed algorithm achieved a great improvement and can meet the needs of online outlier recognition in dam safety management.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-20T05:52:49Z
      DOI: 10.1177/14759217221102060
       
  • Quantifying the extent of local damage of a 60-year-old prestressed
           concrete bridge: A hybrid SHM approach

    • Free pre-print version: Loading...

      Authors: Felipe IH Sakiyama, Gustavo S Veríssimo, Frank Lehmann, Harald Garrecht
      Abstract: Structural Health Monitoring, Ahead of Print.
      The increasing demand for civil infrastructures, the aging of existing assets, and the strengthening of safety and liability laws have led to the inclusion of structural health monitoring (SHM) techniques into the structural management process. With the latest developments in the sensors field and computational power, real-scale SHM systems’ deployment has become logistically and economically feasible. However, it is still challenging to perform a quantitative evaluation of the structural condition based on measured data. The paper addresses recent efforts to associate measured observations with an identification of local stiffness reduction as a global parameter for damage onset and growth. It proposes a hybrid methodology for model updating and damage identification. The proposed methodology is built on data feature extraction using the principal component analysis (PCA), finite element (FE) simulation, and Monte Carlo simulation to quantify the extent of local damage of a 60-year-old prestressed concrete bridge. The methodology allows a sensor-specific quantification of the local stiffness reduction and makes it possible to focus succeeding bridge inspection, recalculation, and repair works on these areas. Even more, the monitoring in combination with the FE model and proposed methodology provides continuous information on developing stiffness reduction and the acuteness of rehabilitation measures.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-19T06:19:57Z
      DOI: 10.1177/14759217221079295
       
  • Bridge inspection component registration for damage evolution

    • Free pre-print version: Loading...

      Authors: Eric L Bianchi, Nazmus Sakib, Craig Woolsey, Matthew Hebdon
      Abstract: Structural Health Monitoring, Ahead of Print.
      There have been great advances in bridge inspection damage detection involving the use of deep learning models. However, automated detection models currently fall short of giving an inspector an understanding of how the damage has progressed from one inspection to the next. The rate-of-change of the damage is a critical piece of information used by engineers to determine appropriate maintenance and rehabilitation actions to prevent structural failures. We propose a simple methodology for registering two bridge inspection videos or still images, collected at different stages of deterioration, so that trained model predictions may be directly measured and damage progression compared. The changes may be documented and presented to the inspector so that they may quickly evaluate key interest regions in the inspection video or image. Three approaches referred to as rigid, deformable, and hybrid image registration methods were experimentally tested and evaluated based on their ability to preserve the geometric characteristics of the referenced image. It was found in all experiments that the rigid, homography-based transformations performed the best for this application over a state-of-the-art deformable registration method, RANSAC-Flow.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-19T01:52:42Z
      DOI: 10.1177/14759217221083647
       
  • Damage detection on the blade of an operating wind turbine via a single
           vibration sensor and statistical time series methods: Exploring the
           performance limits of robust methods

    • Free pre-print version: Loading...

      Authors: Andeas Panagiotopoulos, Tcherniak Dmitri, Fassois D Spilios
      Abstract: Structural Health Monitoring, Ahead of Print.
      The study aims at damage detection on the blade of an operating Vestas V27 wind turbine via a single vibration response sensor under varying Environmental and Operating Conditions (EOCs), and through this, at exploring the performance limits of robust vibration-based Statistical Time Series type methods. Three trailing edge progressive, 15 cm, 30 cm, and 45 cm long, damage scenarios are examined using signals from a 104-day-long measurement campaign. The study is based on three robust methods: an Unsupervised Principal Component Analysis AutoRegressive model–based (U-PCA-AR) method, an Unsupervised Multiple Model AutoRegressive model–based (U-MM-AR) method, and an Unsupervised Principal Component Analysis Multiple Model AutoRegressive model–based (U-PCA-MM-AR) method. Comparisons with a nominal Unsupervised AutoRegressive model–based (U-AR) method and an 8-sensor–based method are also reported. The results of the study are based on a systematic and thorough assessment procedure using 7000 inspection experiments and confirm that single-sensor-based detection is indeed feasible via the aforementioned robust methods, with the best performance achieved by the U-PCA-MM-AR method which reaches up to 100% True Positive Rate at 4%, 1%, and 0% False Positive Rate for the 15, 30, and 45 cm damage scenarios, respectively. This performance is on par with that of the 8-sensor–based method and indicative of the high capabilities offered by the robust vibration-based Statistical Time Series type methods. The sensitivity of the methods with respect to the sensor location and the AutoRegressive model order is also examined.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-15T04:43:56Z
      DOI: 10.1177/14759217221094493
       
  • Comparison of neural networks based on accuracy and robustness in
           

    • Free pre-print version: Loading...

      Authors: Prabakaran Balasubramanian, Vikram Kaushik, Sumaya Y Altamimi, Marco Amabili, Mohamed Alteneiji
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural health monitoring systems must provide accuracy and robustness in predicting the structure’s health using the minimum intervention to ensure commercial viability. Characterization of impact is useful in assessing its severity, deciding if detailed damage analysis is necessary, and re-evaluating the present health of the structure under monitoring with better confidence. In this characterization process, the impact location is significant since some positions within a structure are more sensitive to damage. The inherent noise and uncertainties present in the sensor response pose a substantial hurdle to estimating the external impact correctly. This paper quantitatively compares three of the widely used neural networks, namely, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory network (LSTM), to estimate impact location from the lead zirconate titanate (PZT) sensor response. For this purpose, a square aluminum plate of 500 × 500 mm was equipped with four PZT sensors; each placed 100 mm away in both the plate directions from a corner and impact loads were given on a grid covering the whole plate. The PZT responses were used to train the three neural networks under study here, and their estimations were compared based on the Mean Absolute Error (MAE). In addition, increasing Gaussian noise was added to the PZT responses, and the robustness of the three neural networks was monitored. It was found that the ANN gives better accuracy with a Mean Absolute Error of 22 mm compared to Convolutional Neural Network (MAE = 31 mm) and Long Short-Term Memory (MAE = 25 mm). However, CNN is more robust when encountering noise with a 2% reduction in accuracy, while LSTM and ANN lost 7% and 11% accuracy, respectively.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-13T11:37:48Z
      DOI: 10.1177/14759217221098569
       
  • Spectral variational mode extraction and its application in fault
           detection of rolling bearing

    • Free pre-print version: Loading...

      Authors: Bin Pang, Heng Zhang, Tianshi Cheng, Zhenduo Sun, Yan Shi, Guiji Tang
      Abstract: Structural Health Monitoring, Ahead of Print.
      The core of fault diagnosis of rolling bearing is to extract the narrowband sub-components containing fault feature information from the bearing fault signal. Variational mode extraction (VME), a novel single sub-component separation algorithm originated from variational mode decomposition (VMD), provides a promising solution to bearing fault detection. However, its performance is closely related to the hyperparameter selection, including the center frequency ωd and the penalty factor α. This paper proposes a non-recursive and adaptive signal decomposition algorithm termed spectral variational mode extraction (SVME). SVME can be seen as a spectral decomposition technique whose framework is composed of the adaptive spectral boundary division and boundary constrained VME. In the adaptive spectral boundary division, an adaptive iterative spectral envelope method referring to the continuous envelope correlation (CCE) index is developed to integrate with the parameterless scale-space division to adaptively locate the frequency band boundary. The presented adaptive spectral boundary division approach can effectively inhibit the spectral boundary over-division. In the boundary constrained VME, the dominant frequency of each frequency band determined by the optimal spectral division is distinguished as the preset center frequency. Meanwhile, the optimal penalty factor is determined based on the envelope spectral kurtosis (ESK) index and the boundary-constraint principle. The SVME method is utilized in the simulation and experimental case studies to investigate its capability. Furthermore, its superiority is highlighted through the comparison with the variational mode decomposition (VMD) and Autogram methods.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-13T04:48:41Z
      DOI: 10.1177/14759217221098670
       
  • Adaptive dynamic mode decomposition and its application in rolling bearing
           compound fault diagnosis

    • Free pre-print version: Loading...

      Authors: Ping Ma, Hongli Zhang, Cong Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      The decoupling detection of compound faults in rolling bearing is attracting considerable attentions. In recent years, some time-series decomposition methods, such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), symplectic geometry mode decomposition (SGMD) etc., are used to extract the fault characteristics of bearing fault vibration signal and achieve the purpose of fault diagnosis. However, the fault characteristics of compound fault vibration signals are unevenly distributed, some fault characteristics are weakly disturbed by noise, which limit these methods application in compound fault diagnosis. In this paper, the Koopman operator is introduced from the perspective of flow field dynamics information extraction, an adaptive dynamic mode decomposition (ADMD) is proposed to decompose the nonlinear time-series data into a set of dynamic mode components (DMCs). In ADMD, a high-degree polynomial is applied to fit the data sequence of flow field, and a low dimensional subspace can be obtained. The dominant dynamics of flow field can be represented by the eigenvalues and eigenvectors of the low-dimensional subspace, which can be transformed into reconstructed time series with different frequency, and obtained the frequency-based DMCs. The simulation and experimental analysis results show that the proposed method can effectively decouple different fault components of compound fault of rolling bearing.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-12T10:37:50Z
      DOI: 10.1177/14759217221095729
       
  • Waveform covariance imaging for Lamb wave phased array

    • Free pre-print version: Loading...

      Authors: Caibin Xu, Ning Hu, Mingxi Deng
      Abstract: Structural Health Monitoring, Ahead of Print.
      The amplitude and the phase information are both important for damage localization in Lamb wave–based structural health monitoring and non-destructive testing. Most previous studies in Lamb wave imaging are only based on either the amplitude or phase of the signal at the time of flight. In this study, a post-processing technique for Lamb wave phased array imaging is proposed for isotropic plates, which simultaneously utilizes both the amplitude and phase information within a small neighborhood centered at the time of flight. In the proposed imaging algorithm, a modified virtual time reversal is firstly implemented to compensate for dispersion and the amplitude decrease caused by wave diffusion. Then the waveform covariance between any two processed wave packets, which contains information of both amplitude and phase, is used as the indicator of the presence of damage. Finally, adaptive weights based on minimum variance are introduced to weight and sum the waveform covariance for damage imaging. Based on a uniform circular array, experimental results on an aluminum plate with three defects verify that the proposed algorithm is capable of localizing multiple defects, suppressing background noise and main lobe width.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-10T06:24:11Z
      DOI: 10.1177/14759217221098579
       
  • A method for structural monitoring of multispan bridges using satellite
           InSAR data with uncertainty quantification and its pre-collapse
           application to the Albiano-Magra Bridge in Italy

    • Free pre-print version: Loading...

      Authors: Elisabetta Farneti, Nicola Cavalagli, Mario Costantini, Francesco Trillo, Federico Minati, Ilaria Venanzi, Filippo Ubertini
      Abstract: Structural Health Monitoring, Ahead of Print.
      Synthetic Aperture Radar Interferometry (InSAR) using satellite data is revealing a promising tool for monitoring long-term deformation phenomena in critical infrastructural systems. Nevertheless, its use in structural engineering is still quite limited and a general understanding of its potential is still missing, especially when dealing with bridge structures for which specific methods of data processing and displacement assessment with error quantification need to be developed, accounting for the type of deformation phenomena and for the orientation of the bridge. In order to partly fill this research gap, this paper proposes a post-processing methodology to derive two-dimensional displacement configurations of multi-span bridges with properly defined error bounds, using both ascending and descending Synthetic Aperture Radar acquisitions. In order to obtain an engineering meaningful estimate of the uncertainties affecting the reconstructed bridge deformations, both random and systematic errors are quantified, accounting for the orientation of the bridge with respect to the Line-Of-Sights of the satellites, the hypothesized deformation plane and the accuracy of InSAR measurements. The proposed procedure has been applied to the illustrative case study of the Albiano-Magra Bridge in Italy, collapsed on 8 April 2020. The results, referred to the monitoring period 2015–2020, demonstrate the effectiveness of the proposed method in supporting engineering assessments. In particular, an initially temperature-induced stationary deformation phenomenon has been observed, with all spans moving upwards or downwards during summer and winter. Afterwards, displacements of increasing amplitude for two side spans have been observed during the three years preceding the failure, providing information on the possible cause of collapse.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-07T02:38:53Z
      DOI: 10.1177/14759217221083609
       
  • Engineering deep learning methods on automatic detection of damage in
           infrastructure due to extreme events

    • Free pre-print version: Loading...

      Authors: Yongsheng Bai, Bing Zha, Halil Sezen, Alper Yilmaz
      Abstract: Structural Health Monitoring, Ahead of Print.
      This paper presents a few comprehensive experimental studies for automated Structural Damage Detection (SDD) in extreme events using deep learning methods for processing 2D images. In the first study, a 152-layer Residual network (ResNet) is utilized to classify multiple classes in eight SDD tasks, which include identification of scene levels, damage levels, and material types. The proposed ResNet achieved high accuracy for each task while the positions of the damage are not identifiable. In the second study, the existing ResNet and a segmentation network (U-Net) are combined into a new pipeline, cascaded networks, for categorizing and locating structural damage. The results show that the accuracy of damage detection is significantly improved compared to only using a segmentation network. In the third and fourth studies, end-to-end networks are developed and tested as a new solution to directly detect cracks and spalling in the image collections of recent large earthquakes. One of the proposed networks can achieve an accuracy above 67.6% for all tested images at various scales and resolutions, and shows its robustness for these human-free detection tasks. As a preliminary field study, we applied the proposed method to detect damage in a concrete structure that was tested to study its progressive collapse performance. The experiments indicate that these solutions for automatic detection of structural damage using deep learning methods are feasible and promising. The training datasets and codes will be made available for the public upon the publication of this paper.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-06T03:08:50Z
      DOI: 10.1177/14759217221083649
       
  • Precise damage shaping in self-sensing composites using electrical
           impedance tomography and genetic algorithms

    • Free pre-print version: Loading...

      Authors: Hashim Hassan, Tyler N Tallman
      Abstract: Structural Health Monitoring, Ahead of Print.
      Fiber-reinforced composites with nanofiller-modified polymer matrices have immense potential to improve the safety of high-risk engineering structures. These materials are intrinsically self-sensing because their electrical conductivity is affected by deformations and damage. This property, known as piezoresistivity, has been extensively leveraged for conductivity-based damage detection via electrical resistance change methods and tomographic imaging techniques such as electrical impedance tomography (EIT). Although these techniques are very effective at detecting the presence of damage, they suffer from an inability to provide precise information about damage shape, size, or mechanism. This is particularly detrimental for laminated composites which can suffer from complex failure modes, such as delaminations, that are difficult to detect. To that end, we herein propose a new technique for precisely determining damage shape and size in self-sensing composites. Our technique makes use of a genetic algorithm (GA) integrated with realistic physics-based damage models to recover precise damage shape from conductivity changes imaged via EIT. We experimentally validate this technique on carbon nanofiber (CNF)-modified glass fiber-reinforced polymer (GFRP) laminates by considering two specific damage mechanisms: through-holes (as a function of number, size, and location) and impact-induced delaminations (as a function of impact energy). Our results show that this novel technique can accurately reconstruct multiple through-holes with radii as small as 1.19 mm and delaminations caused by low velocity impacts. The reconstructed delamination shapes and sizes were shown to be in much better agreement with the actual delaminations observed using optical microscopy than is achievable by traditional EIT alone. These findings illustrate that coupling piezoresistivity with conductivity-based spatial imaging techniques and physics-based inversion strategies can enable damage shaping capabilities in self-sensing composite structures.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-05T11:12:45Z
      DOI: 10.1177/14759217221077034
       
  • Vision-guided unmanned aerial system for rapid multiple-type damage
           detection and localization

    • Free pre-print version: Loading...

      Authors: Shang Jiang, Yuyao Cheng, Jian Zhang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Unmanned aerial systems (UASs) are increasingly applied for bridge inspection. A vision-guided UAS with a lightweight convolutional neural network is developed to detect and locate bridge cracks, spalling, and corrosion. The contributions are as follows: (1) To address the problem that traditional UASs are global positioning system (GPS) required while GPS signals under bridge bottom generally are weak. A vision-guided UAS is designed and applied, in which a stereo vision-inertial fusion method is used to provide position data instead of GPS and an ultrasonic ranger is applied to avoid obstacles. (2) Most of the deep learning-based damage detection methods are offline detection, which is unsuitable for UAS-based inspection because the endurance time is limited. To solve this problem, a lightweight end-to-end object detection network is proposed, by replacing the backbone of the original You Only Look Once v3 network with MobileNetv2, and the proposed network of much faster inference speed can be transplanted to the onboard computer of the designed UAS so that real-time edge computing can be performed during inspection. (3) A damage location method based on vision positioning data and simultaneous localization and mapping is also proposed to meet the urgent needs of locating damage in the whole structure. Finally, the proposed system is applied to inspect a long-span bridge to detect and locate the most common damages: crack, spalling, and corrosion with high accuracy and efficiency, which verified the practicability of the system.
      Citation: Structural Health Monitoring
      PubDate: 2022-05-03T04:48:13Z
      DOI: 10.1177/14759217221084878
       
  • An iterative morphological difference product wavelet for weak fault
           feature extraction in rolling bearing fault diagnosis

    • Free pre-print version: Loading...

      Authors: Junchao Guo, Qingbo He, Dong Zhen, Fengshou Gu, Andrew D Ball
      Abstract: Structural Health Monitoring, Ahead of Print.
      Weak fault feature extraction is of great significance to the fault diagnosis of rolling bearing. At the early stage of defects, fault features are usually weak and easily submerged in strong background noise, which makes feature information extremely difficult to be excavated. This paper proposes an iterative morphological difference product wavelet (MDPW) to address this issue. In this scheme, firstly, the morphological difference product filter (MDPF) is developed using the combination morphological filter-hat transform operator and difference operator. The MDPF is then incorporated into a morphological undecimated wavelet to construct the MDPW, which can achieve noise suppression and fault feature enhancement. Subsequently, the optimal iteration numbers that influence the performance of MDPW is determined using the fault severity indicator, which effectively extracts periodic impulse related to the failure of rolling bearing. Finally, the fault identification is inferred by the occurrence of fault defect frequencies in the MDPW spectrum with the optimal iteration numbers. The validity of the iterative MDPW is evaluated through numerical simulations and experiment cases. The analysis results demonstrate that the iterative MDPW has higher diagnosis accuracy than existing algorithms (e.g., adaptive single-scale morphological wavelet and weighted multi-scale morphological wavelet). This research provides a new perspective for improving the weak fault feature extraction of rolling bearing.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-26T07:33:42Z
      DOI: 10.1177/14759217221086314
       
  • Contact delamination detection of anisotropic composite plates using
           non-elliptical probability imaging of nonlinear ultrasonic guided waves

    • Free pre-print version: Loading...

      Authors: Yuan Liu, Xiaobin Hong, Bin Zhang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Contact delamination damage easily occurs inside the composite. The extension and accumulation of the damage occupy most of the time from damage appearance to failure. However, the anisotropy of materials and the small size of early damage make accurate detection difficult. In this paper, a non-elliptical probability imaging (NEPI) method based on nonlinear ultrasonic guided waves is proposed for the delamination damage detection in anisotropic composites. Firstly, the anisotropic characteristics of the composite are analyzed theoretically, and the velocity in all directions is obtained by numerical simulation. Secondly, the arrival time is obtained by the intersection of the upper and lower envelope fitting lines, and nonlinear coefficient is calculated based on the smooth pseudo Wigner–Ville distribution (SPWVD). Then, the time coefficient (CT) and nonlinear damage index (NDI) are defined and applied in the NEPI. By comparison with a reference point, the problem that the damage location of anisotropic composite cannot be solved analytically is avoided skillfully. Finally, the NEPI method is used to present the damage. The experiment results show that the NEPI method can accurately display the damage location. The sensitivity and reliability of NDI based on SPWVD are further verified by comparison with damage index of scattered signal and time–frequency analysis methods such as fast Fourier transform, short-time Fourier transform, and S-transform. The proposed NEPI method based on nonlinear ultrasonic guided waves can detect the delamination damage with good location accuracy and high damage sensitivity.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-26T07:22:23Z
      DOI: 10.1177/14759217221085159
       
  • Damage identification of wind turbine blades using the microphone array
           under different parametric and measuring conditions: A prototype study
           with laboratory-scale models

    • Free pre-print version: Loading...

      Authors: Shilin Sun, Tianyang Wang, Hongxing Yang, Fulei Chu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural health monitoring (SHM) of wind turbine blades is significant to the reliability and efficiency of wind energy generation, and it is a challenging issue due to the complicated structures and variational operating conditions. In this investigation, a SHM method for wind turbine blades based on the microphone array and acoustic source identification is proposed. With the equipment of loudspeakers in blade cavities, damage-related information is excited to be captured by the array. To generate accurate acoustic maps with high spatial resolutions, a novel algorithm for sparsity-based sound field reconstruction is developed based on the generalized minimax-concave penalty function. With a laboratory-scale wind turbine model, damage identification performance of the proposed method is evaluated under different parametric and measuring conditions, and experiments are conducted under diverse blade health conditions. Results reveal that and both internal and external damage in operating blades can be recognized as acoustic sources, and satisfactory performance of the proposed method can be guaranteed with appropriate parameters. Furthermore, determination criteria for parameters are concluded with respect to the variation of measuring conditions. This prototype study provides useful insights into the development of effective SHM systems.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-18T12:28:28Z
      DOI: 10.1177/14759217221085655
       
  • Characterization of OFDR distributed optical fiber for crack monitoring
           considering fiber-coating interfacial slip

    • Free pre-print version: Loading...

      Authors: Lizhi Zhao, Fujian Tang, Hong-Nan Li, Farhad Ansari
      Abstract: Structural Health Monitoring, Ahead of Print.
      An analytical crack-strain transfer model considering interfacial bond-slip between optical fiber and fiber coating is proposed and experimentally validated for crack monitoring of structures. Interfacial shear stress-slip relationship between the optical fiber and fiber coating was determined by using pull-out tests, and a simplified trilinear three-stage interfacial shear stress-slip model was proposed. The model is incorporated into the development of a crack-strain transfer model between the structural substrate and the optical fiber, and the fiber strain distribution is analytically obtained and compared with the strain distribution as perfect bond is assumed between the fiber and fiber coating. Experiments were also conducted to validate the crack-strain transfer model by continuously monitoring corrosion-induced concrete cracking with OFDR distributed optical fiber. Compared with strain transfer models proposed by other researchers in the literature and perfect bond model, the crack-strain transfer model considering interfacial shear stress-slip mechanism between fiber and fiber coating agrees well with experimental results, especially for large crack monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-18T05:32:32Z
      DOI: 10.1177/14759217221085155
       
  • Monitoring of new and existing stainless-steel reinforced concrete
           structures by clad distributed optical fibre sensing

    • Free pre-print version: Loading...

      Authors: Ignasi Fernandez, Carlos Gill Berrocal, Sebastian Almfeldt, Rasmus Rempling
      Abstract: Structural Health Monitoring, Ahead of Print.
      The implementation of structural health monitoring (SHM) systems in existing civil engineering structures could contribute to a safer and more resilient infrastructure as well as important savings. Due to their light weight, small size, and high resistance to the environment, distributed optical fibre sensors (DOFS) stand out as a very promising technology for damage detection and quantification in reinforced concrete (RC) structures. In this paper, the performance of DOFS featuring an external polymeric cladding with rough surface, to accurately assess deflection and crack width of a stainless-steel RC beam subjected to four-point bending is investigated. Several sensor positions, both embedded in the concrete and attached to the surface, are investigated in a multi-layer configuration. The study revealed that embedded sensors yield very accurate results regardless of the sensor position and the load level, that is, service or ultimate loads, being the sensor glued in a premade groove on the steel bar the most reliable solution for high-load levels. Conversely, externally deployed sensors for the assessment of existing structures, described attenuated values for the measured deflections, and, to some extent also the crack width, due to a loss of bond between the sensor and the surrounding concrete, already for service loads. Corrective methods to further use the obtained data are presented, yet the clad DOFS attached to the concrete surface described a significant drop of performance with increasing load levels, showing an important loss of data at 80% of the ultimate load.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-18T03:01:34Z
      DOI: 10.1177/14759217221081149
       
  • A convolutional neural network for pipe crack and leak detection in smart
           water network

    • Free pre-print version: Loading...

      Authors: Chi Zhang, Bradley J. Alexander, Mark L. Stephens, Martin F. Lambert, Jinzhe Gong
      Abstract: Structural Health Monitoring, Ahead of Print.
      The implementation of a smart water network (SWN) is viewed as a strategic approach to address many challenges faced by water utilities, such as pipe leak detection and main break prevention. This paper develops a convolutional neural network (CNN)–based model to classify acoustic wave files collected by the South Australian Water Corporation’s (SA Water’s) SWN over the city of Adelaide. The VGGish model (VGG refers to the team who developed the model—Visual Geometry Group) is selected as a suitable transfer learning model to extract features from wave files. The CNN model classifies an acoustic wave file as an anomaly or other background or environmental noise. Identification of a wave file as an anomaly triggers a Siamese CNN model to determine whether it is related to a regular/irregular scheduled event (for example, irrigation system near public parks or water consumption by large buildings). A field investigation is initiated if a wave file is classified as an anomaly and it is not related to a scheduled event. The developed models have been validated using data that is recorded by SWN in Adelaide. This validation data set comprises 1098 wave files, which are recorded by 34 accelerometers and are associated with 32 known leaks. The validation results shown that accuracy of alarms generated by the developed models is 92.44%. The validations confirm the developed models as an effective tool for water pipeline leak and crack detection, which, in turn, enables proactive management of the pipeline assets.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-15T08:44:01Z
      DOI: 10.1177/14759217221080198
       
  • A CNN-integrated percussion method for detection of FRP–concrete
           interfacial damage with FEM reconstruction

    • Free pre-print version: Loading...

      Authors: Qingzhao Kong, Keyan Ji, Jiaxuan Gu, Lin Chen, Cheng Yuan
      Abstract: Structural Health Monitoring, Ahead of Print.
      Reinforced concrete (RC) structures are commonly strengthened using externally bonded fiber-reinforced polymer (FRP) sheets. The bond between the FRP and concrete is a crucial factor affecting the strengthening effect, and debonding along the FRP–concrete interface is usually accompanied by the fracture of the underlying concrete. Therefore, it is necessary to identify the interface damage of FRP-to-concrete joints and conduct mechanical analysis. However, debonding is invisible damage that occurs inside the underlying FRP layer, which makes damage detection more difficult. To this end, this study fuses a percussion method with a deep learning framework to address the detection of such invisible lesions. Meanwhile, the visualization study provides guidance for later maintenance work. To further illustrate the hazard of the identified lesions, three-dimensional reconstruction for finite element modeling (FEM) with detected damage information based on percussion is proposed to elucidate the mechanical degradation caused by the fracture of underlying concrete. Lastly, the results of this study demonstrate that the detection, visualization, and FEM reconstruction of FRP–concrete interface damage using percussion signals has considerable application potential and is worthy of further study.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-15T01:54:08Z
      DOI: 10.1177/14759217221082007
       
  • Long-term friction performance monitoring of sliding layer in China
           railway track system Ⅱ slab track on bridge superstructure

    • Free pre-print version: Loading...

      Authors: Shunlong Li, Dedao Wang, Chao Lin, Senrong Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      The sliding layer is an important component of the China Railway Track System (CRTS) Ⅱ slab track on bridges, and its friction performance has a significant influence on the intensity of track–bridge interaction (TBI). However, the friction performance deterioration of this sliding layer under actual repeated abrasion and harsh environmental conditions is difficult to grasp since structural health monitoring implementation into in-service high-speed railway tracks is extremely hard to be permitted and only limited valuable monitoring datasets are available at present. In this study, the friction performance degradation of the sliding layer was therefore investigated using long-term monitoring data from a multi-span simply supported box girder bridge. First, the friction-induced strain in the base plate was decoupled using the monitored strain at the fixing point and mid-span based on mechanical properties of TBI. Then, structural health monitoring and finite element analysis both indicated an approximately linear relationship between the decoupled friction-induced strain and the temperature of the investigated bridge under certain circumstances. Furthermore, the correspondence between the slope of this linear relationship and the friction coefficient was modelled. Finally, the friction coefficients of the sliding layers on target spans were identified using 4 years of monitoring data and the established correspondence model to analyse the statistical characteristics and degradation performance of the layers. This investigation of the friction performance degradation of the sliding layer in the CRTS II slab track system provides guidance for future maintenance and replacement decision making.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-14T03:44:49Z
      DOI: 10.1177/14759217221081558
       
  • Integrated interval Mahalanobis classification system for the quality
           classification of turbine blades based on vibrational data incorporating
           measurement uncertainty

    • Free pre-print version: Loading...

      Authors: Liangliang Cheng, Vahid Yaghoubi, Wim Van Paepegem, Mathias Kersemans
      Abstract: Structural Health Monitoring, Ahead of Print.
      Measurements are not exactly accurate, and measurement errors could lead to a biased trained classifier, and finally to a wrong classification of the parts. This paper extends the recently proposed (Integrated) Mahalanobis Classification System with the concept of Interval Mahalanobis distance (IMD) in order to account for measurement uncertainty. This novel Integrated Interval Mahalanobis Classification System (IIMCS) is applied to an experimental case study of complex shaped metallic turbine blades with various damage types. The turbine blades have been vibrationally tested in a wide frequency range. The IIMCS selects a subset of optimal features that contribute the most to the system under the framework of Binary Particle Swarm Optimization, and determines the optimal decision threshold based on Particle Swarm Optimizer. A Monte Carlo method (MCM) is implemented to account for measurement uncertainty, and as such yields an indicator of reliability, implying the confidence level of the classification results. The obtained results illustrate a high performance of the IIMCS for classifying turbine blades based on vibrational response data with measurement uncertainty.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-13T09:31:56Z
      DOI: 10.1177/14759217221076366
       
  • Fault feature recognition of centrifugal compressor with cracked blade
           based on SNR estimation and adaptive stochastic resonance

    • Free pre-print version: Loading...

      Authors: Di Song, Feiyun Xu, Jianzhong Hu, Tianchi Ma
      Abstract: Structural Health Monitoring, Ahead of Print.
      The centrifugal compressor is widely used in modern industry, and the blades are prone to crack due to complex loads and working conditions. Owing to the fault features caused by initial blade cracks are week and easily interfered by strong noise, it is difficult to recognize accurately by traditional methods. In this study, the SNR estimation and adaptive stochastic resonance (SEASR) method is proposed for fault feature recognition of centrifugal compressor with cracked blade. Based on the relationship between SNR and stochastic resonance (SR) parameters, the subtract noise by empirical mode decomposition (SNEMD) method is established with second-order and fourth-order moment (M2M4), hence solving the problem of adaptive SR. The effectiveness of SEASR is tested and analyzed by simulation signals and experimental data. The results demonstrate that the proposed method can accurately recognize fault features of cracked blades and compound faults, where the output SNR is superior to that of the previous methods. It is a new method to realize fault diagnosis for centrifugal compressor with cracked blade and other rotating machinery.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-11T07:00:18Z
      DOI: 10.1177/14759217221084880
       
  • Stiffness identification method for asphalt pavement layers and interfaces
           using monitoring data from built-in sensors

    • Free pre-print version: Loading...

      Authors: Xianyong Ma, Zejiao Dong, Yongkang Dong
      Abstract: Structural Health Monitoring, Ahead of Print.
      The bearing capacity of asphalt pavement gradually deteriorates due to repeated traffic loads. As the crucial mechanical index to evaluate the bearing capacity, the evolution of the stiffness of pavement layers and interfaces is necessary to be mastered. This paper proposes a stiffness identification method for the asphalt pavement layers and interfaces by using monitoring data from built-in sensors. First, the analytical solution of multi-layered elastic/viscoelastic medium with imperfect interlayer subjected to moving load is derived, and the theoretical relationship between stiffness and mechanical response can be obtained. The sensor layout is optimized on the basis of the theoretical relationship. Then, the stiffness identification method is developed, and its feasibility is theoretically explored through an example of three-layered elastic/viscoelastic medium. Finally, the proposed stiffness identification method is applied to a realistic asphalt pavement to validate its reliability. Results show that the proposed method can evaluate the stiffness of pavement layers (including elastic and viscoelastic properties) and interfaces. It is noteworthy that the stiffness identification method by using monitoring data from built-in sensors can be performed in real-time under each passing vehicle load, and is helpful to understand the damage behavior of pavement and guide maintenance decision-making.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-07T01:24:58Z
      DOI: 10.1177/14759217221077612
       
  • Damage localization for bridges monitored within one cluster based on a
           spatiotemporal correlation model of strain monitoring data

    • Free pre-print version: Loading...

      Authors: Jianxin Cao, Yang Liu
      Abstract: Structural Health Monitoring, Ahead of Print.
      The bridges monitored within one cluster refer to several medium- and small-span beam bridges with similar structural characteristics located in a continuous elevated corridor. The variation in the strain monitoring data of these bridges comprehensively reflects diverse coupling effects. These complex coupling factors present great challenges for the damage diagnosis of bridges. To address this issue, a damage localization method for bridges monitored within one cluster is proposed based on a spatiotemporal correlation model of strain monitoring data between bridges. First, a deep learning architecture combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network is established, which can reveal the complex time-varying mapping relationship between the strain monitoring data for similar bridges within one cluster to obtain an accurate spatiotemporal correlation model. Second, a strain prediction framework is presented that uses the proposed spatiotemporal correlation model after training. On this basis, the predicted and measured strains can be utilized to calculate a damage localization index that is not affected by complex coupling factors. Then, combined with abnormal diagnosis theory, the proposed index is implemented to accurately localize damage in all bridges within one cluster. Finally, the application of the proposed method to three actual bridges monitored within one cluster demonstrates the accuracy of the spatiotemporal correlation model and the effectiveness of structural damage localization.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-06T04:06:30Z
      DOI: 10.1177/14759217221078766
       
  • Adaptive guided wave-based damage identification under unknown load
           conditions

    • Free pre-print version: Loading...

      Authors: Xuyun Ding, Xiaojun Wang, Yongbo Yu, Wenpin Chen, Linxi Zeng
      Abstract: Structural Health Monitoring, Ahead of Print.
      Damage identification methods based on guided waves (GWs) have been widely researched in the field of aircraft structural health monitoring. Notably, the existing research has not extensively considered the realization of accurate damage localization in an unknown load environment, although this aspect is of significance to the real-time safety assessment of aircraft structures. To address this issue, we propose an adaptive damage imaging method based on GW signals for typical aircraft structures subjected to unknown workloads. First, through the GW signals and load data sampled in the working load range, a mathematical model between the characteristics of the GW signal and load is established. Second, the active GW signal is adaptively compensated by the real-time identified load through the passive strain signal during the service process of the structure. Finally, the alternating time-reversal phase synthesis (ATRPS) method is used to accurately locate the damage in the monitoring area. The feasibility, applicability, and accuracy of the proposed methodology are validated by three levels of experimental cases. All results consistently indicate that reliable damage localization can be achieved under an unknown load environment.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-06T01:17:35Z
      DOI: 10.1177/14759217221078946
       
  • Unsupervised dam anomaly detection with spatial–temporal variational
           autoencoder

    • Free pre-print version: Loading...

      Authors: Xiaosong Shu, Tengfei Bao, Yuhang Zhou, Ruichen Xu, Yangtao Li, Kang Zhang
      Abstract: Structural Health Monitoring, Ahead of Print.
      The anomaly detection and health monitoring of dams have attracted increasing attention. To detect the temporal and spatial anomalies of the dam, a novel spatial–temporal variational autoencoder is proposed. The proposed model is based on the sequential variational autoencoder, and its backbone is fulfilled by the recurrent neural network and graph convolutional network to capture the temporal and spatial features in both the generative and inference models. To obtain a normal pattern, we made an assumption that the normal values should be temporally smooth and spatially similar. Then, the smoothness and similarity-inducing operations are used in the framework of the proposed model. Through the addition of smoothness and similarity losses in sequential variational autoencoder, the proposed model can obtain a temporally smooth and spatially similar pattern. For verification, an arch dam is taken as an example. Through comparison among six baseline models, the proposed model detects the temporal and spatial anomalies accurately and stably.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-05T01:51:38Z
      DOI: 10.1177/14759217211073301
       
  • Intelligent fault diagnosis of rotating machinery using composite
           multivariate-based multi-scale symbolic dynamic entropy with multi-source
           monitoring data

    • Free pre-print version: Loading...

      Authors: Yu Wei, Xianzhi Wang, Yuanbo Xu, Fan Fan
      Abstract: Structural Health Monitoring, Ahead of Print.
      Fault diagnosis of rotating machinery plays a significant role in the reliability and safety of modern industrial systems, which generally requires collaborative fault diagnosis by features extracted from multiple sensors since the multi-channel vibration signals carry a wealth of fault information. However, there is a remaining obstacle for fault diagnosis of multi-source monitoring data: integration of multisensory data. Hence, a novel framework is proposed for fault diagnosis of multi-source monitoring data. First, composite multivariate multi-scale symbolic dynamic entropy is proposed to extract fault features. Second, Laplacian score is introduced to select the distinguishing features with better clustering ability. Finally, the selected features are fed into a logistic regression classifier so that various faults of machinery are diagnosed. The simulation and two case studies using gearbox and pump data are performed to validate and demonstrate the superiority of the proposed method.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-05T01:30:09Z
      DOI: 10.1177/14759217221079668
       
  • Reweighted-Kurtogram with sub-bands rearranged and ensemble dual-tree
           complex wavelet packet transform for bearing fault diagnosis

    • Free pre-print version: Loading...

      Authors: Xin Zhang, Zhongqiang Zhang, Jiaxu Wang, Zhiwen Liu, Lei Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      The Fast Kurtogram (FK) is a widely used resonance demodulation technique for bearing fault diagnosis. In this paper, a novel method termed Reweighted-Kurtogram with sub-bands rearranged and ensemble dual-tree complex wavelet packet transform (SRE-DTCWPT) is proposed to improve the performance of the FK from the aspects of band division and optimal band selection indicator. To obtain an excellent band division, the SRE-DTCWPT is first developed. It retains the main advantages of DTCWPT and meanwhile addresses the two key issues of frequency sub-bands disorder and frequency bands leakage. Then, a new robust evaluating indicator called reweighted kurtosis is defined. It solves the problem of kurtosis being sensitive to strong impulse interferences. Furthermore, the proposed method involves a set of envelope analysis approaches developed on different cases of fault signals to realize the enhanced identification of the bearing diagnostic information. Two simulated signals and actual bearing signals regarding different practical cases are employed to investigate the effectiveness of the proposed method. In addition, the proposed method is compared with the FK, and the results verify that the proposed method shows high potentials for extracting bearing diagnostic information from complex vibration signals.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-04T12:31:13Z
      DOI: 10.1177/14759217211069197
       
  • Separate modeling technique for deformation monitoring of concrete dams

    • Free pre-print version: Loading...

      Authors: Chuan Yin, Zhenyu Wu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Deformation monitoring is an important aspect of safety control for concrete dams. Deformation monitoring models (such as statistical models and hybrid models) are extensively applied to predict concrete dam deformation and derive confidence interval of normal deformation for anomaly detection. Deformation monitoring models for concrete dams mainly consist of hydrostatic component, temperature component, and aging component. The optimum parameters of individual components are simultaneously determined by least square method for monitoring data fitting. Thus, significant over-fitting of deformation monitoring models may be induced by mutual compensation among the parameters of model components. In this paper, the Separate Modeling Technique (SMT) is proposed for mitigating the over-fitting problem of deformation monitoring models for concrete dams. Firstly, the Empirical Mode Decomposition (EMD) is adopted to extract and visualize the aging component of displacement sequence. Proper mathematical formulation of the aging component can be established, and the problem of improperly presupposing the mathematical form of aging component in the process of constructing traditional deformation monitoring models is well addressed. In this study, the hydrostatic component is represented by the Hybrid Response Surface (HRS), which is formulated using numerical simulation with varying water levels and material parameters. The displacement variation caused by water level fluctuation is identified in terms of isothermal conditions and is used to calibrate the material parameters in the HRS. The temperature component is separated through subtracting the hydrostatic and aging components from displacement time series and then is expressed with proper mathematical formulations. Finally, hybrid models for displacement monitoring of concrete dams are established by combining the separately formulated components. The Separate modeling technique is applied to formulate crest displacement of the YL concrete gravity dam. The false alarm rate of displacement monitoring and a new model selection criterion (namely over-fitting coefficient) are adopted to compare various deformation monitoring models. It is shown that the over-fitting levels of deformation monitoring models can be effectively reduced using the SMT. The deformation monitoring models constructed with the SMT are of better accuracy in displacement prediction and present no false alarm of displacement monitoring for the tested period.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-04T01:31:58Z
      DOI: 10.1177/14759217221079013
       
  • Nonlinear damage identification method of transmission tower structure
           based on general expression for linear and nonlinear autoregressive model
           and Itakura distance

    • Free pre-print version: Loading...

      Authors: Heng Zuo, Huiyong Guo
      Abstract: Structural Health Monitoring, Ahead of Print.
      Fatigue cracks and bolt looseness are two kinds of common nonlinear damage in a transmission tower structure. However, due to the complexity of the transmission tower structure, it is difficult to identify the nonlinear damage accurately by using traditional damage identification methods. To solve this problem effectively, a time domain damage identification method based on general expression for linear and nonlinear autoregressive model (GNAR model) and Itakura distance is proposed. To describe the stochastic characteristics of time series more concisely and accurately, the optimized structure of GNAR model was selected by the stochastic pruning algorithm based on greedy strategy. And Itakura distance was used as a damage indicator for nonlinear damage identification. The nonlinear damage experiment of three-story frame model in Los Alamos laboratory was used to verify the effectiveness of the proposed method, and this method was applied to the nonlinear damage identification experiment of a transmission tower steel frame model. In the transmission tower model experiment, two kinds of nonlinear damage types are considered: component breathing cracks and joint bolt loosening. The results show that the proposed nonlinear damage identification method can easily identify the nonlinear damage of the frame model and the transmission tower model effectively. The change of floor mass barely has effects on the damage identification results. The damage probability of the damaged stories calculated by the proposed method is significantly higher than that of the undamaged stories, so that it is helpful to find the location of the nonlinear damage source efficiently. And the proposed method is a damage identification method based on sub-structure story, which can identify the transmission tower model with two nonlinear damage sources at the same time.
      Citation: Structural Health Monitoring
      PubDate: 2022-04-01T11:23:25Z
      DOI: 10.1177/14759217211073496
       
  • A self-matching model for online anomaly recognition of safety monitoring
           data in dams

    • Free pre-print version: Loading...

      Authors: Fang Zhang, Xiang Lu, Yanling Li, Zhiliang Gao, Han Zhang, Huibao Huang
      Abstract: Structural Health Monitoring, Ahead of Print.
      The online anomaly recognition of real-time dam safety monitoring data, such as deformation and seepage data from the automatic sensing instruments (e.g., the osmometer and the multi-point displacement meter), has the premise of ensuring data reliability, and it is also one of the core functional modules of online dam safety monitoring. To compensate for the limitation of a single method to identify outliers and further improve the reliability and the rapidity of the anomaly recognition of dam safety monitoring data, a self-matching model based on data-types for online anomaly recognition (SMM) was proposed in this paper. Based on a detailed classification of dam safety monitoring data sequences, this article describes a comparison and analysis of the applicability of a statistical regression model based on the least-squares regression (LSR) model and the online robust recognition and early warning (RREW) model for different datatype sequences. For the single-step-type sequences and normal-type sequences with low fitting accuracy, which could not be completely identified by the two models above, an improved cloud model recognition method based on the diurnal variation rate (ICM) was proposed to compensate for the limitations. Finally, the SMM was determined, that is, the LSR model was used for the multi-point-outlier-type and normal-type sequences with high fitting accuracy, the RREW model method was used for the double-step-type and oscillatory-type sequences, and the ICM method was used for the single-step-type sequences and normal-type sequences with low fitting accuracy. The engineering application of the Dadu River Basin showed that this method effectively solved the problems of low calculation efficiency and a 2% misjudgment rate when using the RREW model alone, and this method greatly improved the accuracy and timeliness of the anomaly recognition of dam safety monitoring data, so it had important theoretical significance and engineering application value.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-29T07:17:23Z
      DOI: 10.1177/14759217221074603
       
  • Bayesian dynamic linear model framework for structural health monitoring
           data forecasting and missing data imputation during typhoon events

    • Free pre-print version: Loading...

      Authors: Qi-Ang Wang, Chang-Bao Wang, Zhan-Guo Ma, Wei Chen, Yi-Qing Ni, Chu-Fan Wang, Bing-Gang Yan, Pei-Xuan Guan
      Abstract: Structural Health Monitoring, Ahead of Print.
      A Bayesian dynamic linear model (BDLM) framework for data modeling and forecasting is proposed to evaluate the performance of an operational cable-stayed bridge, that is, Ting Kau Bridge in Hong Kong, by using SHM strain field data acquired. One of the major challenges in dealing with the existing in-service bridge under extreme typhoon loads is to forecast structural behavior using the typhoon response exhibiting non-stationarity, large data fluctuations and strong randomness. The first attempt for SHM data modeling during extreme events, that is, typhoons, using BDLM framework, was conducted in this study. The data from multiple sensors are analyzed for one-step, multi-step ahead forecasting and missing data imputation. The overall bridge behavior is incorporated into a forecasting model by superposition of forecasting results of trend (representing the structural baseline response), periodic component (response component evolving regularly over time), and autoregressive component (time-dependent error) through BDLM algorithm. The results demonstrate that the BDLM framework yielded more accurate calculations compared with Gaussian process and Variational Heteroscedasticity Gaussian Process methods with respect to one-step ahead forecasting for strain data under typhoons. Multi-step ahead forecasting was successfully carried out both for non-typhoon and typhoon responses within an acceptable precision range. The correlation between periodic component and temperature was also investigated. Regarding missing data imputation, BLDM algorithm can generate robust results due to making full use of the monitoring data both before and after the missing segments.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-28T05:07:03Z
      DOI: 10.1177/14759217221079529
       
  • Three decades of statistical pattern recognition paradigm for SHM of
           bridges

    • Free pre-print version: Loading...

      Authors: Eloi Figueiredo, James Brownjohn
      Abstract: Structural Health Monitoring, Ahead of Print.
      Bridges play a crucial role in modern societies, regardless of their culture, geographical location, or economic development. The safest, economical, and most resilient bridges are those that are well managed and maintained. In the last three decades, structural health monitoring (SHM) has been a promising tool in management activities of bridges as potentially it permits one to perform condition assessment to reduce uncertainty in the planning and designing of maintenance activities as well as to increase the service performance and safety of operation. The general idea has been the transformation of massive data obtained from monitoring systems and numerical models into meaningful information. To deal with large amounts of data and perform the damage identification automatically, SHM has been cast in the context of the statistical pattern recognition (SPR) paradigm, where machine learning plays an important role. Meanwhile, recent technologies have unveiled alternative sensing opportunities and new perspectives to manage and observe the response of bridges, but it is widely recognized that bridge SHM is not yet fully capable of producing reliable global information on the presence of damage. While there have been multiple review studies published on SHM and vibration-based structural damage detection for wider scopes, there have not been so many reviews on SHM of bridges in the context of the SPR paradigm. Besides, some of those reviews become obsolete quite fast, and they are usually biased towards applications falling outside of bridge engineering. Therefore, the main goal of this article is to summarize the concept of SHM and point out key developments in research and applications of the SPR paradigm observed in bridges in the last three decades, including developments in sensing technology and data analysis, and to identify current and future trends to promote more coordinated and interdisciplinary research in the SHM of bridges.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-27T05:27:15Z
      DOI: 10.1177/14759217221075241
       
  • Self-monitoring of stresses in grouted sleeves using smart grout

    • Free pre-print version: Loading...

      Authors: Guofu Qiao, Jiongfeng Sun
      Abstract: Structural Health Monitoring, Ahead of Print.
      Currently, grouted sleeves are widely used in the connection of prefabricated reinforced concrete (RC) members. The mechanical performance of a connection joint is critical to the safety and durability of structures, and traditional monitoring techniques have many limitations. This paper proposes a stress self-monitoring approach based on the smart grout in which the structural and sensing functions are perfectly combined. A theoretical model was proposed to address the relationship between the sensing sensitivity and the uniaxial tensile load. The influence of the rebar embedment length on the sensitivity was investigated. Then, experiments were performed to verify the self-monitoring performance of smart grouted sleeves. Moreover, the stress self-monitoring performance of the smart grouted sleeves was compared to that of traditional grouted sleeves. The results revealed that the sensitivity increased with the uniaxial tension, and the sensitivity decreased as the embedded length of the rebar in the sleeves decreased in the theoretical model. The effectiveness of the proposed model was verified with a comparison between the calculated and experimental results. When the rebar yielded, the value of the damage coefficient ω was less than 3%, and the sensing sensitivity was close to 20% in the pull-out test. When the destruction approached, the value of ω exceeded 7% or more. This investigation provides a theoretical and experimental basis for the application of the smart grouted sleeves in actual engineering.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-27T03:35:42Z
      DOI: 10.1177/14759217221080516
       
  • Heterogeneous data fusion for the improved non-destructive detection of
           steel-reinforcement defects using echo state networks

    • Free pre-print version: Loading...

      Authors: Adam J Wootton, Charles R Day, Peter W Haycock
      Abstract: Structural Health Monitoring, Ahead of Print.
      The degradation of roads is an expensive problem: in the United Kingdom alone, £27 billion was spent on road repairs between 2013 and 2019. One potential cost-saver is the early, non-destructive detection of faults. There are many available techniques, each with its own benefits and drawbacks. This paper builds upon the successful processing of magnetic flux leakage (MFL) data by echo state networks (ESNs) for damage diagnostics, by augmenting ESNs with the depth of concrete cover as part of a data fusion approach. This fusion-based ESN outperformed a number of non-fusion ESN comparators and a previously used analytical technique. Additionally, the fusion ESN had an optimal threshold value whose standard deviation was three times smaller than that of the nearest alternative technique, potentially prompting a move towards automated defect detection in ‘real-world’ applications.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-26T05:57:54Z
      DOI: 10.1177/14759217221080718
       
  • Robust fault diagnosis of rolling bearings via entropy-weighted nuisance
           attribute projection and neural network under various operating conditions
           

    • Free pre-print version: Loading...

      Authors: Di Yang, Yong Lv, Rui Yuan, Hewenxuan Li, Weihang Zhu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Rolling bearings are crucial components in the fields of mechanical, civil, and aerospace engineering. They sometimes work under various operating conditions, which makes it harder to distinguish faults from normal signals. Nuisance attribute projection (NAP) is a technique that has been widely used in audio and image recognition to eliminate interference information in the extracted feature space. In constructing the weighted matrix of NAP, the setting of the weighted value represents the degree of interference between the feature vectors. The interference is either taken into consideration in whole, or not considered at all, which will inevitably lead to information loss. In our work, an entropy-weighted NAP (EWNAP) is proposed to deal with such “bipolar problem” in constructing the weighted matrix. The eigenvalues of covariance matrix of collected signals contain dynamical information, and the fuzzy entropy is adopted to evaluate the dispersion degree of these eigenvalues. After normalization, these entropy values are used to express the weight relationship in the weighted matrix of EWNAP. The features processed by EWNAP can be used as samples and combined with neural network to achieve fault diagnosis of rolling bearings. Furthermore, a fault diagnosis approach with insufficient data is demonstrated to validate the effectiveness of the proposed scheme. In the case studies, Case Western Reserve University bearing database and data collected from the bearing fault simulation bench are used. These case studies show that the proposed EWNAP alleviates the interference caused by various operating conditions, and the comparative analysis confirms that the proposed method works better than the conventional methods.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-25T10:27:53Z
      DOI: 10.1177/14759217221077414
       
  • A new GW-based heteroscedastic Gaussian process method for online crack
           evaluation

    • Free pre-print version: Loading...

      Authors: Hui Wang, Shenfang Yuan, Qiuhui Xu, Yixing Meng, Yuanqiang Ren
      Abstract: Structural Health Monitoring, Ahead of Print.
      Accurate evaluation of fatigue crack by using structural health monitoring (SHM) methods is very important to the life management of aircraft structures. However, fatigue crack propagation is always a complicated process for individual aircraft structures during their long-term service. And the monitoring has to be performed under different environmental and operational conditions. These factors result in the uncertain distribution of the damage index obtained by SHM. The distribution usually changes along with the service time, which can be defined as heteroscedasticity. The heteroscedasticity characteristic of the uncertain distribution of damage index has an important negative impact on the SHM based diagnostics algorithms if not considered during the evaluation. However, till now, few researchers have considered this important aspect. To improve the evaluation accuracy under different environmental and operational conditions for individual aircraft, this paper proposes a new guided wave-based heteroscedastic Gaussian process method. Gaussian process quantile regression is adopted to estimate the conditional quantiles of the damage index distribution during the service to deal with the heteroscedasticity. The method is validated on an attachment lug fatigue test, an important aircraft structural component. The experimental results demonstrate that the proposed method can quantify the heteroscedastic uncertainty associated and obviously improve the quality of crack evaluation. For the serious heteroscedastic specimen, the maximum evaluation is only 0.7 mm reduced from the original 7.4 mm, which is an order of magnitude.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-22T05:39:32Z
      DOI: 10.1177/14759217221076740
       
  • Damage detection on a historic iron bridge using satellite DInSAR data

    • Free pre-print version: Loading...

      Authors: PF Giordano, ZI Turksezer, M Previtali, MP Limongelli
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural Health Monitoring (SHM) allows tracking the structural behavior in time and support decisions regarding, for instance, the need for maintenance and repair activities. Most traditional SHM systems require sensors that are directly applied to the structure to get insights into the structural performance. Satellite technologies can provide an appealing alternative to traditional SHM. They allow to measure displacements at a large scale and to follow their evolution without the need of directly accessing the structure. Further to this, the possibility to monitor large areas opens new avenues for the development of automatic alert systems able to issue an alarm and early-flag damaged structures. However, displacements of civil structures might also be induced by sources other than damage such as thermal or periodic hydrogeological variations. These can hinder the onset and development of damage or lead to false alarms if such displacements are erroneously interpreted as damages. This paper aims to present a new method for damage detection based on DInSAR measurements, that tackles both aspects providing reliable information about the onset of damage under environmentally changing conditions in a period corresponding to about twice the revisit time of the satellite. A case study is presented to demonstrate the applicability of the proposed method, namely the Palatino bridge in Rome, Italy. The satellite data are acquired by COSMO-SkyMed of the Italian Space Agency and consist of displacements of the observed structure recorded during a period spanning between 2011 and 2019.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-18T08:12:33Z
      DOI: 10.1177/14759217211054350
       
  • Looseness monitoring of multiple M1 bolt joints using multivariate
           intrinsic multiscale entropy analysis and Lorentz signal-enhanced
           piezoelectric active sensing

    • Free pre-print version: Loading...

      Authors: Rui Yuan, Yong Lv, Tao Wang, Si Li, Hewenxuan Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      Bolts are widely used in the fields of mechanical, civil, and aerospace engineering. The condition of bolt joints has a significant impact on the safe and reliable operation of the whole equipment. The failure of bolt joints monitoring leads to severe accidents or even casualties. This paper proposes a novel bolt joints monitoring method using multivariate intrinsic multiscale entropy (MIME) analysis and Lorentz signal-enhanced piezoelectric active sensing. Lorentz signal is used as excitation signal in piezoelectric active sensing to expose nonlinear dynamical characteristics of the bolt joints. Multivariate variational mode decomposition (MVMD) is employed to decompose multiple components of the collected Lorentz signal into multivariate band-limited intrinsic mode functions (BLIMFs). Afterward, improved multiscale sample entropy (IMSE) values of each channel’s BLIMFs are computed to measure its irregularity and complexity. IMSE values are taken as quantitative features, reflecting dynamical characteristics of bolt joints. Further, the constructed 3-layer feature matrices are adopted as the input of the convolutional neural network (CNN) to achieve accurate bolt joint monitoring. The multiple M1 bolt joints are used during the experiment to verify the effectiveness and superiority of the proposed approach. The results demonstrate the proposed novel approach is promising in bolt joints monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-16T12:34:03Z
      DOI: 10.1177/14759217221088492
       
  • Compatibility and challenges in machine learning approach for structural
           crack assessment

    • Free pre-print version: Loading...

      Authors: Intisar Omar, Muhammad Khan, Andrew Starr
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural health monitoring and assessment (SHMA) is exceptionally essential for preserving and sustaining any mechanical structure’s service life. A successful assessment should provide reliable and resolute information to maintain the continuous performance of the structure. This information can effectively determine crack progression and its overall impact on the structural operation. However, the available sensing techniques and methods for performing SHMA generate raw measurements that require significant data processing before making any valuable predictions. Machine learning (ML) algorithms (supervised and unsupervised learning) have been extensively used for such data processing. These algorithms extract damage-sensitive features from the raw data to identify structural conditions and performance. As per the available published literature, the extraction of these features has been quite random and used by academic researchers without a suitability justification. In this paper, a comprehensive literature review is performed to emphasise the influence of damage-sensitive features on ML algorithms. The selection and suitability of these features are critically reviewed while processing raw data obtained from different materials (metals, composites and polymers). It has been found that an accurate crack prediction is only possible if the selection of damage-sensitive features and ML algorithms is performed based on available raw data and structure material type. This paper also highlights the current challenges and limitations during the mentioned sections.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-11T04:57:11Z
      DOI: 10.1177/14759217211061399
       
  • Damage severity assessment in composite structures using multi-frequency
           lamb waves

    • Free pre-print version: Loading...

      Authors: Anvesh R Nandyala, Ashish K Darpe, Satinder P Singh
      Abstract: Structural Health Monitoring, Ahead of Print.
      When a narrowband tone burst excitation signal is used to generate Lamb waves in the thin composite plates, a suitable frequency of the excitation signal is typically chosen to excite a solitary mode of Lamb wave in the structure. However, if and as the damage severity changes in the structure, it may be possible that the frequency of the excitation signal may influence the response metric. In the present work, the use of a chirp signal as an excitation signal for damage severity assessment is proposed. The use of a chirp signal as an excitation signal may prove suitable for damage severity assessment because the chirp response contains a large frequency bandwidth, and the cumulative effect of all the frequencies with the delamination is reflected in the damage index. The sensitivity of the chirp response to varying delamination sizes is investigated through numerical simulations and experiments. The damage index calculated using the chirp response showed a monotonic trend with an increase in damage severity, in contrast to a bidirectional trend of the damage index when conventional narrow excitation signals are used.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-08T11:31:35Z
      DOI: 10.1177/14759217221076736
       
  • Probabilistic machine learning for detection of tightening torque in
           bolted joints

    • Free pre-print version: Loading...

      Authors: Luccas P Miguel, Rafael de O Teloli, Samuel da Silva, Gaël Chevallier
      Abstract: Structural Health Monitoring, Ahead of Print.
      Observing the loss of tightening torque using modal parameters is challenging due to the variability and nonlinear effects in bolted joints. Thus, this paper proposes a combined application of two probabilistic machine learning methods. First, a Gaussian mixture model (GMM) is learned using estimated natural frequencies, assuming the tightening torque in a safe situation. This probabilistic model can assuredly detect the lack of torque using indirect vibration measures in other unknown states by computing a damage index. A Gaussian process regression (GPR) is also learned considering a set of torque and damage index pairs in several conditions. The GPR model interpolates a curve to supply an estimative of the tightening torque for other conditions not used in this learning. An illustrative application is performed considering the Orion beam, an academic-scale specimen composed of a lap-joint configuration that retains the friction surface in contact patches. The structure is subjected to a random vibration with a controlled RMS level and several tightening torque conditions to identify the modal parameters. The probabilistic model learning via the GMM and GPR can detect adequately, with a low number of false diagnoses, the actual state of torque using an indirect measure of vibration, that is, without the need for a torque sensor on each bolt.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-04T11:41:33Z
      DOI: 10.1177/14759217211054150
       
  • Integrating visual sensing and structural identification using 3D-digital
           image correlation and topology optimization to detect and reconstruct the
           3D geometry of structural damage

    • Free pre-print version: Loading...

      Authors: Mehrdad S Dizaji, Devin K Harris, Mohamad Alipour
      Abstract: Structural Health Monitoring, Ahead of Print.
      This paper describes a novel technique for detecting internal or unseen damage in structural steel members by combining measurements from full-field three-dimensional digital image correlation (3D-DIC) with a topology optimization framework. Unlike the majority of conventional methods that rely on specialized forms of surface-penetrating waves or radiation imaging, this work employs optical cameras to measure surface strains and deformations using the 3D-DIC technique followed by an optimization approach to detect the existing damage. This data-rich representation of the structural component’s behavior is then used to reconstruct the underlying subsurface abnormalities via an inverse mechanical problem. The inverse problem is solved using a topology optimization formulation that iteratively adjusts a fine-tuned finite element model (FEM) of the structure to reveal irregularities within it. Having recently demonstrated the feasibility of detecting and reconstructing defects in small-scale structural components, this paper expands on the authors' previous work to demonstrate the feasibility and performance of the proposed method through an experimental program in which a set of large-scale structural steel beams with and without buried defects tested using a full-field 3D DIC sensing approach. The structure’s initial FEM is first created to discretize the member into elements whose constitutive properties are treated as unknowns in the optimization problem. The goal of the optimization is to minimize the discrepancies between the observed full-field response measured experimentally using DIC and that computed numerically using the model. To that end, an objective function is first computed as the sum of residuals by mapping both responses onto a common grid, which is then pushed to a minimum via the method of moving asymptotes (MMA) as the optimization algorithm. This study demonstrates that the proposed approach can identify unseen damage with an average accuracy (ACC) score of 96.80% on the defined configurations, with relatively minimal false identifications.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-04T06:00:14Z
      DOI: 10.1177/14759217211073505
       
  • Structural deformation prediction model based on extreme learning machine
           algorithm and particle swarm optimization

    • Free pre-print version: Loading...

      Authors: Shouyan Jiang, Linxin Zhao, Chengbin Du
      Abstract: Structural Health Monitoring, Ahead of Print.
      In this paper, an extreme learning machine (ELM) algorithm based on particle swarm optimization (PSO) is proposed to predict structural deformation. Taking an aqueduct located in Tiantai County, Zhejiang, China, as a case study, a series of observations of the aqueduct vertical displacements and crack openings were used to train a neural network. Then, variables representing environmental factors (air temperature), hydraulic factors (water level), and aging were selected as the influence factors input into the prediction model. Finally, the proposed PSO–ELM model was used to predict the vertical deformation and crack opening of the aqueduct, and the predicted results were compared with the monitored values using four evaluation indexes: mean absolute error (MAE), mean squared error (MSE), maximum absolute error (S), and correlation coefficient (R). The prediction results obtained using the PSO–ELM model were then compared with those obtained using the evolutionary ELM, conventional ELM, back propagation neural network, long short-term memory, and multiple linear regression models. The results indicate that the proposed PSO–ELM model has an evidently superior predictive ability, with higher values of R and lower values of MAE, MSE, and S. The proposed model can therefore be confidently used to serve as a tool similar to a “weather forecast” function to predict the vertical deformation and crack openings of an aqueduct and may be employed for other structural monitoring applications as well.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-03T12:14:51Z
      DOI: 10.1177/14759217211072237
       
  • A multiple-point monitoring model for concrete dam displacements based on
           correlated multiple-output support vector regression

    • Free pre-print version: Loading...

      Authors: Qiubing Ren, Mingchao Li, Shuo Bai, Yang Shen
      Abstract: Structural Health Monitoring, Ahead of Print.
      Displacements reflect the overall behavior of a concrete dam; thus, it is of vital importance to evaluate the overall structural health status by displacement-based mathematical monitoring models. However, most of the existing monitoring models focus on point-by-point displacement modeling, ignoring the correlations among displacements at different measurement points. This study therefore proposes a model for dam multiple-point displacement monitoring based on the support vector regression (SVR) algorithm. The improved SVR-based model with multiple-output formulation is a new development based on the statistical learning theory, which can simultaneously analyze and predict displacements at multiple-measurement points. Furthermore, by introducing the weight vectors that separate the common and individual information, the potential correlations among multiple-point displacements can be fully exploited by the multiple-output SVR. Combining the above two improvements, a multiple-point monitoring model for dam displacements considering spatiotemporal correlations, referred to as correlated multiple-output SVR (CMOSVR), is constructed. The proposed model is verified using in-situ monitoring from a full-scale concrete gravity dam. The accuracy, robustness, and efficiency of the CMOSVR-based model are compared with those of conventional single-point monitoring models, such as classical hydrostatic-seasonal-time model and standard SVR-based model. Empirical results show that in both real and simulated noisy scenarios, the CMOSVR-based multiple-point model can achieve a better monitoring performance with less modeling time cost. Moreover, the superior performance of CMOSVR-based model does not require a very strong correlation among multiple-point displacements, which considerably improves the adaptability of the monitoring model to various possible scenarios. The novel multiple-point model will provide an effective technical support tool for ensuring the safe operation of dams.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-02T06:54:34Z
      DOI: 10.1177/14759217211069639
       
  • Attention recurrent residual U-Net for predicting pixel-level crack widths
           in concrete surfaces

    • Free pre-print version: Loading...

      Authors: Aravinda S Rao, Tuan Nguyen, Son T Le, Marimuthu Palaniswami, Tuan Ngo
      Abstract: Structural Health Monitoring, Ahead of Print.
      Cracks in concrete structures are one of the most important indicators of structural damage, and it is a necessity to detect and measure cracks for ensuring safety and integrity of concrete structures. The widely practised approach in inspecting the structures is by performing visual inspections followed by manual estimation of crack widths. This approach is not only time-consuming, laborious, and time-intensive but also prone to subjective errors and inefficient. To address these issues, we propose a novel deep learning framework for detecting cracks and then estimating crack widths in concrete surface images. Our framework handles both small- and large-sized images and provides a prediction of crack width at locations specified by the user. The proposed framework uses Attention Recurrent Residual U-Net (Attention R2U-Net) with Random Forest regressor to predict crack width with the mean prediction error of ±0.31 mm for crack widths varying from 0 to 8.95 mm and produces the lowest absolute maximum error of 1.3 mm. Our model has a coefficient of determination (R2) of 0.91, showing a non-linear mapping function with low prediction errors. We compare our model with a combination of four other segmentation models and regression models. Our proposed model has superior performance compared to other models, and one can easily adopt our framework to a variety of Structural Health Monitoring applications using Internet of Things sensors.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-01T07:24:34Z
      DOI: 10.1177/14759217211068859
       
  • A new quantitative acoustic emission model for damage characterization of
           composite laminates using original waveforms

    • Free pre-print version: Loading...

      Authors: Dong Xu, Pengfei Liu, Zhiping Chen, Tao Wu
      Abstract: Structural Health Monitoring, Ahead of Print.
      The correlation between acoustic characteristics and mechanical behaviors shows great significance for health monitoring and characterization. This paper develops a new quantitative model based on the modified Mel-frequency cepstral analysis and statistical methods so as to link acoustic emission (AE) features with mechanical behaviors of end-notched flexure (ENF) composite laminates. First, the Mel-frequency cepstral analysis in automatic speech recognition is modified to adapt to AE sensors and signals. Second, the modified Mel-frequency cepstral coefficients (MFCCs) are extracted from original waveforms of AE hits for damage characterization of composites. MFCC0 is taken as an effective feature to qualitatively discriminate damage stages and to identify the pre-failure critical point. The decreasing patterns of MFCC1 and MFCC2 for ENF specimens can be clearly observed with the loading time by using the simple moving average method. Third, pencil lead breaks are repeatedly conducted on the healthy specimen to verify the pattern in the degraded specimen. Finally, a further investigation based on the cumulative moving average method demonstrates that MFCC1 and MFCC2 are quadratic and linear functions of the load ratio or the deflection ratio, respectively. In addition, the latter is more suitable to be an indicator of damage accumulation of composite laminates.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-01T04:35:44Z
      DOI: 10.1177/14759217211056566
       
  • Automatic defect detection for ultrasonic wave propagation imaging method
           using spatio-temporal convolution neural networks

    • Free pre-print version: Loading...

      Authors: Jiaxing Ye, Nobuyuki Toyama
      Abstract: Structural Health Monitoring, Ahead of Print.
      Ultrasonic wave propagation imaging enables the detection of anomalies in various structures; hence, it has been applied as one of the promising techniques for damage identification in structural health monitoring (SHM). The interpretation of imaging data is vital to SHM; however, it relies significantly on expert subjective judgment, rendering the results vulnerable to human errors. Recent advances in the field of computer vision arising from the adoption of deep neural networks have resulted in new perspectives for substituting human roles in laborious data interpretation tasks. This paper presents an effective learning architecture that can characterize the ultrasonic wave propagation videos for automatic non-destructive inspection. The main contribution is threefold: 1. To the best of our knowledge, this is the first study to leverage video content analysis techniques to exploit ultrasonic wave propagation image series. Previous approaches that focused on the still wavefield images are likely to lose critical temporal information, thereby resulting in an inferior performance. 2. We devise a model that progressively aggregates both temporal and spatial information encoded in multiple adjacent snapshots of ultrasonic wave propagation motions for efficient data analysis. We presented the details regarding the system implementation and critical parameter settings. 3. The proposed approach is validated through extensive experimental comparisons with other state-of-the-art computer vision techniques on a real dataset which is publicly available. We hope that this study will encourage further investigations into video-based non-destructive data interpretation, not limited to ultrasonic signals.
      Citation: Structural Health Monitoring
      PubDate: 2022-03-01T01:40:35Z
      DOI: 10.1177/14759217211073503
       
  • Statistics-based baseline-free approach for rapid inspection of
           delamination in composite structures using ultrasonic guided waves

    • Free pre-print version: Loading...

      Authors: Tabjula L Jagadeeshwar, Sheetal Kalyani, Prabhu Rajagopal, Balaji Srinivasan
      Abstract: Structural Health Monitoring, Ahead of Print.
      Delamination in composite structures is characterized by a resonant cavity wherein a fraction of an ultrasonic guided wave may be trapped. Based on this wave trapping phenomenon, we propose a baseline-free statistical approach for the identification and localization of delamination using sparse sampling and density-based spatial clustering of applications with noise (DBSCAN) technique. The proposed technique can be deployed for rapid inspection with minimal human intervention. The Performance of the proposed technique in terms of its ability to determine the precise location of such defects is quantified through the probability of detection measurements. The robustness of the proposed technique is tested through extensive simulations consisting of different random locations of defects on flat plate structures with different sizes and orientation as well as different values of signal to noise ratio of the simulated data. The simulation results are also validated using experimental data and the results are found to be in good agreement.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-28T07:21:37Z
      DOI: 10.1177/14759217211073335
       
  • Non-parametric empirical machine learning for short-term and long-term
           structural health monitoring

    • Free pre-print version: Loading...

      Authors: Alireza Entezami, Hashem Shariatmadar, Carlo De Michele
      Abstract: Structural Health Monitoring, Ahead of Print.
      Early damage detection is an initial step of structural health monitoring. Thanks to recent advances in sensing technology, the application of data-driven methods based on the concept of machine learning has significantly increased among civil engineers and researchers. On this basis, this article proposes a novel non-parametric anomaly detection method in an unsupervised learning manner via the theory of empirical machine learning. The main objective of this method is to define a new damage index by using some empirical measure and the concept of minimum distance value. For this reason, an empirical local density is initially computed for each feature and then multiplied by the minimum distance of that feature to derive a new damage index for decision-making. The minimum distance is obtained by calculating the distances between each feature and training samples and finding the minimum quantity. The major contributions of this research contain developing a novel non-parametric algorithm for decision-making under high-dimensional and low-dimensional features and proposing a new damage index. To detect early damage, a threshold boundary is computed by using the extreme value theory, generalized Pareto distribution, and peak-over-threshold approach. Dynamic and statistical features of two full-scale bridges are used to verify the effectiveness and reliability of the proposed non-parametric anomaly detection. In order to further demonstrate its accuracy and proper performance, it is compared with some classical and recently published anomaly detection techniques. Results show that the proposed non-parametric method can effectively discriminate a damaged state from its undamaged condition with high damage detectability and inconsiderable false positive and false negative errors. This method also outperforms the anomaly detection techniques considered in the comparative studies.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-28T07:07:04Z
      DOI: 10.1177/14759217211069842
       
  • 1D-CNN-based damage identification method based on piezoelectric impedance
           using adjustable inductive shunt circuitry for data enrichment

    • Free pre-print version: Loading...

      Authors: Xin Zhang, Hui Wang, Borui Hou, Jiawen Xu, Ruqiang Yan
      Abstract: Structural Health Monitoring, Ahead of Print.
      The electromechanical impedance (EMI)-based damage identification method is a non-destructive testing approach in the field of structural health monitoring. The frequency response function (FRF) of EMI can effectively reveal the health conditions of a structure. Typically, the health condition is identified by comparing the FRF of a structure to that of a baseline. However, baselines may exhibit unpredictable shifts in real applications. In this study, a new EMI-based health identification method is proposed without reference to baselines or handcrafted features. An adjustable inductive shunt circuit that can enrich the EMI dataset is connected to a piezoelectric transducer. Pre-set damage, including bolt looseness and mass variations, are selected to demonstrate damage identification. The FRFs are extracted using a phase-sensitive detection algorithm. The damage identification model is realized using a one-dimensional convolutional neural network. Experimental results show that the proposed method can identify the location of bolt loosening and mass variation with an overall accuracy of 99.24%. The proposed method can be applied for identifying the health conditions of a structure with strong nonlinearity.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-24T12:05:20Z
      DOI: 10.1177/14759217211049720
       
  • CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for
           vibration-based fault recognition

    • Free pre-print version: Loading...

      Authors: Vahid Yaghoubi, Liangliang Cheng, Wim Van Paepegem, Mathias Kersemans
      Abstract: Structural Health Monitoring, Ahead of Print.
      Nowadays, using vibration data in conjunction with pattern recognition methods is one of the most common fault detection strategies for structures. However, their performances depend on the features extracted from vibration data, the features selected to train the classifier, and the classifier used for pattern recognition. Deep learning facilitates the fault detection procedure by automating the feature extraction and selection, and classification procedure. Though, deep learning approaches have challenges in designing its structure and tuning its hyperparameters, which may result in a low generalization capability. Therefore, this study proposes an ensemble deep learning framework based on a convolutional neural network (CNN) and Dempster–Shafer theory (DST), called CNN-DST. In this framework, several CNNs with the proposed structure are first trained, and then, the outputs of the CNNs selected by the proposed technique are combined by using an improved DST-based method. To validate the proposed CNN-DST framework, it is applied to an experimental dataset created by the broadband vibrational responses of polycrystalline nickel alloy first-stage turbine blades with different types and severities of damage. Through statistical analysis, it is shown that the proposed CNN-DST framework classifies the turbine blades with an average prediction accuracy of 97.19%. The proposed CNN-DST framework is benchmarked with other state-of-the-art classification methods, demonstrating its high performance. The robustness of the proposed CNN-DST framework with respect to measurement noise is investigated, showing its high noise-resistance. Further, bandwidth analysis reveals that most of the required information for detecting faulty samples is available in a small frequency range.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-23T02:22:35Z
      DOI: 10.1177/14759217211050012
       
  • Bayesian dynamic regression for reconstructing missing data in structural
           health monitoring

    • Free pre-print version: Loading...

      Authors: Yi-Ming Zhang, Hao Wang, Yu Bai, Jian-Xiao Mao, Yi-Chao Xu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Massive data that provide valuable information regarding the structural behavior are continuously collected by the structural health monitoring (SHM) system. The quality of monitoring data is directly related to the accuracy of the structural condition assessment and maintenance decisions. Data missing is a common and challenging issue in SHM, compromising the reliability of data-driven methods. Thus, the accurate reconstruction of missing SHM data is an essential step for the reliable evaluation of the structural condition. Data recovery can be considered as a regression task by modeling the correlation among sensors. The Bayesian linear regression (BLR) model has been extensively used in probabilistic regression analysis due to its efficiency and the ability of uncertainty quantification. However, because of the fixed coefficients (refer to a static model) and linear assumption, the BLR model fails to accurately capture the relationship and accommodate the changes in related variables. Given this limitation, this study presents a Bayesian dynamic regression (BDR) method to reconstruct the missing SHM data. The BDR model assumes that the linear form is only locally suitable, and the regression variable varies according to a random walk. In particular, the multivariate BDR model can reconstruct the missing data of different sensors simultaneously. The Kalman filter and expectation maximum (EM) algorithms are employed to estimate the state variables (regressors) and parameters. The feasibility of the multivariate BDR model is demonstrated by utilizing the data from a building model and a long-span cable-stayed bridge. The results show that the multivariate BDR model exhibits excellent performance to rebuild the missing data in terms of both computational efficiency and accuracy. Compared to the standard BLR and linear BDR models, the quadratic BDR model owns better reconstruction accuracy.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-12T07:14:16Z
      DOI: 10.1177/14759217211053779
       
  • Detection of shallow wall-thinning of pipes using a flexible interdigital
           transducer-based scanning laser Doppler vibrometer

    • Free pre-print version: Loading...

      Authors: To Kang, Seong-Jin Han, Soonwoo Han, Kyung-Mo Kim, Dong-Jin Kim
      Abstract: Structural Health Monitoring, Ahead of Print.
      Interdigital transducer (IDT)-based scanning laser Doppler vibrometers (SLDVs) have recently been developed to detect shallow defects in plates. An IDT-based SLDV was augmented with Lamb wave frequency fusion capability to enable the imaging of defects across the entire depth of a plate. Dry-coupled IDTs are a type of field-deployable IDT that do not require the attachment of a couplant to the structure. However, this technique is not applicable to the analysis of shallow wall thinning, because IDTs composed of lead zirconium titanate (PZT) cannot be attached to the curved surface of a structure. To address this issue, we developed a new type of IDT—the flexible IDT—that can be bent along the circumferential direction of a pipe. To set the gap spacing of the flexible IDT, the wavenumber sensitivity and minimum distance from the reference mode (MDRM) was calculated, and the S0 mode is selected to ensure high-wavenumber sensitivity above the threshold of the MDRM. The proposed flexible IDT-based SLDV was tested by using it to visualize the wall thinning on pipes with 4%, 6%, and 20% thinning. The results of this study will contribute toward the development of systems that improve on existing planar laser scanning schemes and enable shallow defect detection in pipes using laser ultrasonics.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-12T01:36:33Z
      DOI: 10.1177/14759217211067830
       
  • Simulation analysis method of expandable and flexible sensor networks
           based on the flexible printed circuit process

    • Free pre-print version: Loading...

      Authors: Shuguang Hu, Yu Wang, Tao Xiong, Chongqi Wang, Yongan Huang, Lei Qiu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Aircraft structural health monitoring (SHM) technology is a revolutionary technology based on sensor networks, which has the advantages of ensuring flight safety, extending structural life, and reducing maintenance costs. Due to the large size of aircraft structures and large deformation of morphing wings, the sensor networks integrated into the aircraft structures should be large-area, expandable, and flexible. Island-interconnect structure design in flexible electronics is expected to realize such sensor networks, with the help of large-area and low-cost flexible printed circuit (FPC) process. Mechanical analysis can guide the design of island-interconnect sensor networks. However, few studies have been reported to analyze the mechanical properties of whole island-interconnect sensor networks. This paper proposes a simulation analysis method based on the finite element method for the mechanical analysis of expandable and flexible sensor networks. This method mainly includes two parts: extracting the buckling modes through linear buckling analysis and obtaining the deformation states and mechanical properties through postbuckling behavior analysis. It is applied on the mechanical analysis of island-interconnect sensor networks with different geometry designs, including serpentine, fractal, and irregular island-interconnect sensor networks. Tensile experiments of island-interconnect sensor networks manufactured by the FPC process show that the deformation of experimental results is basically consistent with that of simulation results. In conclusion, the proposed simulation analysis method is appliable for the mechanical analysis of serpentine, fractal, and irregular island-interconnect structure networks, guiding the structural design of expandable and flexible sensor networks for large-area SHM of aircraft.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-10T02:39:47Z
      DOI: 10.1177/14759217211068893
       
  • Structural health monitoring of fastener hole using ring-design
           direct-write piezoelectric ultrasonic transducer

    • Free pre-print version: Loading...

      Authors: Voon-Kean Wong, Menglong Liu, Wei-Peng Goh, Shuting Chen, Zheng Zheng Wong, Fangsen Cui, Kui Yao
      Abstract: Structural Health Monitoring, Ahead of Print.
      Fatigue cracks initiated from fastener holes are common in aircraft structures. Implementation of effective structural health monitoring (SHM) system to detect or monitor fatigue cracks near fastener holes is desired for realizing condition-based maintenance with improved aircraft safety at reduced cost. In this work, direct-write piezoelectric ultrasonic transducers were used for monitoring crack near fastener hole. Made of poly (vinylidenefluoride-co-trifluoroethylene) [P(VDF-TrFE)] film and annular array electrodes, the direct-write piezoelectric ultrasonic transducers were both directly coated and patterned around the fastener holes. A novel ring-design using annular array electrodes with small footprint was proposed to detect fatigue crack initiated in the vicinity of a fastener hole using pulse-echo and pitch-catch methods. The ring-design direct-write piezoelectric ultrasonic transducers were designed to operate with Lamb wave modes at 1.5 MHz. A numerical simulation study was conducted to investigate the interaction of Lamb wave modes with the fatigue crack. Experimental ultrasonic testing was performed with signal gates determined using wavelet analysis. Fatigue crack detection was demonstrated using an energy ratio method by comparing energy parameter of gated ultrasonic signal with baseline signal. Using the pulse-echo method, the direction of the fatigue crack was able to be determined. The pitch-catch method was found to have higher sensitivity in fatigue crack detection but could not determine the direction of the fatigue crack. These transducers made of thin films promise high conformability even on curved surface and around irregular objects with limited space, compared to conventional discrete ultrasonic transducers. The analysis and results showed that the ring-design direct-write piezoelectric ultrasonic transducers have great potential for fastener hole SHM.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-08T02:57:14Z
      DOI: 10.1177/14759217211073950
       
  • Crack damage monitoring for compressor blades based on acoustic emission
           with novel feature and hybridized feature selection

    • Free pre-print version: Loading...

      Authors: Di Song, Feiyun Xu, Tianchi Ma
      Abstract: Structural Health Monitoring, Ahead of Print.
      Nowadays, acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) for compressor blades. However, traditional AE features and feature selection methods are generally difficult to identify the cracked blades of compressor due to its complex structure and background noise. To solve this problem, the crack damage monitoring method based on novel feature and hybridized feature selection is proposed to identify crack of compressor blades, which is aimed at improving the crack identification accuracy with optimal features. First, the novel feature of spectral centroid with energy shift (SCES) is established. Besides, the hybridized feature selection method is proposed based on Laplacian random forest scores (LRFS), which can evaluate and select features adaptively. By fusing information of selected features from AE sensors, the long short-term memory (LSTM) network is used to classify cracked blades. The proposed method is applied experimentally to identify cracks at different speeds and locations of AE sensors, which has the average accuracy of 98.93%. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM for compressor blades under different working conditions.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-04T03:40:07Z
      DOI: 10.1177/14759217211068107
       
  • Health condition monitoring of bearings based on multifractal spectrum
           feature with modified empirical mode decomposition-multifractal detrended
           fluctuation analysis

    • Free pre-print version: Loading...

      Authors: Guangyi Chen, Changfeng Yan, Jiadong Meng, Zonggang Wang, Lixiao Wu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Multifractal detrended fluctuation analysis (MFDFA) is proved to be a powerful tool for fault diagnosis of rotating machinery due to its ability to reveal multifractal structures hidden in nonstationary and nonlinear vibration signals. To overcome the discontinuity of the fitting scale-dependent trend and the poor adaptability of this algorithm, Empirical Mode Decomposition-Multifractal Detrended Fluctuation Analysis (EMD-MFDFA) is introduced. However, EMD-MFDFA runs into difficulties in reverse segmentation and the selection of the expected Intrinsic Mode Functions (IMFs). Aiming at solving these deficiencies, a Modified EMD-MFDFA (MEMD-MFDFA) approach with IMF selection strategy and Step-Moving Window (SMW) segmentation method is proposed in this paper. In MEMD-MFDFA, a metric for distinguishing deterministic and random components is established to select expected IMF components by scaling exponent. Meanwhile, SMW segmentation method is exploited to reduce the estimated errors caused by reverse segmentation. The robustness of the proposed method is investigated through comparing MEMD-MFDFA, MFDFA, and EMD-MFDFA by multifractality of simulated signals with different Signal-to-Noise Ratio (SNR). Furthermore, the proposed approach is applied to three bearing run-to-failure datasets containing three types of faults, and the results show that the multifeatures of the multifractal spectrum obtained by MEMD-MFDFA have the ability to simultaneously identify early fault and assess performance degradation of bearings.
      Citation: Structural Health Monitoring
      PubDate: 2022-02-01T05:38:59Z
      DOI: 10.1177/14759217211065991
       
  • A novel structural damage identification method based on the acceleration
           responses under ambient vibration and an optimized deep residual algorithm
           

    • Free pre-print version: Loading...

      Authors: Osama Alazzawi, Dansheng Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Recently, structural health monitoring (SHM) methods for civil structures have been investigated widely, especially Deep Learning (DL)-based methods. However, it is usually difficult to fully train a deep neural network, and thus, typical DL-based SHM methods are limited in terms of performance. While addressing these issues, in this paper, a novel methodology is proposed for smart damage identification of frame structures. The newly proposed SHM method is based on raw time-domain structural response signals and deep residual network (DRN). The introduced DRN algorithm has been designed and tested in an effective way for extracting and learning the optimum features of the 1D raw ambient vibration acceleration signals, without any need for engineered features. Also, the network’s performance has been optimized using Bayesian optimization, which clearly enhances the network’s accuracy and information flow across it. Next, the outputs of DRNs are further utilized through new methods for damage size estimation and damage localization. The proposed methodology has been evaluated using the datasets of numerical and experimental frames of the SHM benchmark problem and the dataset of a real-world full-scale truss bridge. The results show that the proposed method is capable of detecting, localizing, and quantifying structural damage accurately for all of the simulated cases of the two examples. Furthermore, conducted comparison studies have approved that the new approach is more efficient than other machine learning-based methods, and it can overcome the major limitations of Artificial intelligence-based SHM models.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-31T11:38:56Z
      DOI: 10.1177/14759217211065009
       
  • Model-based damage identification with simulated transmittance deviations
           and deep learning classification

    • Free pre-print version: Loading...

      Authors: Panagiotis Seventekidis, Dimitrios Giagopoulos
      Abstract: Structural Health Monitoring, Ahead of Print.
      Damage detection and identification is one of the main tasks in vibration based Structural Health Monitoring (SHM). The robustness of such SHM applications depends among others on the amount and quality of data that can be acquired. Model-based SHM methods may offer such data in unlimited numbers by simulating different structural states; however the main drawback remains the accuracy of models especially for small damages and early detection scenarios. In the present work, a method is presented where SHM data is generated though Finite Element (FE) models, simulating transmittance deviations from reference healthy states. The method is tested on a Carbon Fiber Reinforced Polymer truss for multiple damage scenarios of relatively realistic and small magnitude, affecting different truss members. The transmittance deviations for each scenario are approximated in the FE model by reducing the stiffness of the corresponding components simulating in parallel different uncertainties, resulting in a rich training dataset. The simulated data is finally passed to a Deep Learning (DL) classifier which is later validated on the experimental damages. The dataset is proven to provide to the DL classifier the appropriate information to generalize on the experimental states and the method has potential to contribute to model-based SHM applications. The numerical to experimental generalization is proven to depend on the uncertainty simulation of various model parameters.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-28T09:31:25Z
      DOI: 10.1177/14759217211054348
       
  • A novel strategy using optimized MOMED and B-spline based
           envelope-derivative operator for compound fault detection of the rolling
           bearing

    • Free pre-print version: Loading...

      Authors: Yuanbo Xu, Yongbo Li, Youming Wang, Yu Wei, Zhaoxing Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      The bearing regularly suffers from compound faults in real-world working conditions. In comparison to the single-fault feature extraction, the compound fault diagnosis is more difficult to achieve. This paper suggests an alternative signal processing strategy using the Multipoint Optimal Minimum Entropy Deconvolution method (MOMED) and B-spline based envelope-derivative operator (EDO) tools. As an upgraded version of the Minimum Entropy Deconvolution tool, the MOMEDA technique has been extensively available for bearing and gear fault detection. However, this approach results in an open problem related to how one can choose an appropriate filter size. Considering this problem, an optimized MOMED based on Salp Swarm Algorithm is proposed. Besides, a novel energy operator method called B-spline based envelope-derivative operator (B-spline EDO) is proposed to detect the corresponding fault characteristics from the two separated mono-component signals produced by the optimized MOMED. The new B-spline EDO method accomplishes higher fault detection performance in a noisy environment. Finally, the experimental results displayed that the novel compound fault detection approach can effectively identify the compound fault characteristics.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-28T09:21:02Z
      DOI: 10.1177/14759217211062826
       
  • On the use of Vibrational Hill Charts for improved condition monitoring
           and diagnosis of hydraulic turbines

    • Free pre-print version: Loading...

      Authors: Weiqiang Zhao, Alexandre Presas, Mònica Egusquiza, David Valentín, Eduard Egusquiza, Carme Valero
      Abstract: Structural Health Monitoring, Ahead of Print.
      To cope with the intermittent power supply of the new renewable energies and demand fluctuations, Francis turbines are required to operate more and more in an extended operating range, far away from the design point. With this operating behavior, it is very complex to interpret the trend of vibration parameters typically used in Condition Monitoring and to define reasonable alarm and trip levels valid for all the operating range of the unit working in steady conditions. As in the efficiency curves of Francis turbines represented as a function of net head and load (Hill Chart), in this paper we propose to represent the most relevant vibration parameters in surfaces, called Vibrational Hill Charts, which allow a more accurate evaluation of the indicators and their trends and a better classification of abnormal values. To show the potential of Vibrational Hill Charts, a complete database obtained after 2 years of monitoring a large Francis Unit (444 MW rated power) has been used. The mapping of the relevant vibration parameters has been performed by means of Artificial Neural Networks. It is shown that by setting the action levels based on the resulting maps, rather than a constant value, a better diagnosis capacity is achieved as the Receiver Operating Characteristic will be improved. Furthermore, phenomena such as erosive cavitation, which is hard to be detected, could be also assessed with the use of multidimensional analysis based on the Vibrational Hill Chart. As a conclusion, with the Vibrational Hill Chart, the condition monitoring and diagnosis of hydraulic turbines could be improved.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-22T10:44:48Z
      DOI: 10.1177/14759217211072409
       
  • A vibration-based approach for damage identification and monitoring of
           prefabricated beam bridges

    • Free pre-print version: Loading...

      Authors: Fei Han, Danhui Dan, Zhaoyuan Xu, Zichen Deng
      Abstract: Structural Health Monitoring, Ahead of Print.
      As one of the most sophisticated structural types in rapid industrialized bridge construction technology, the fabricated beam bridge has been widely used in road network system for a long time. The prominent disease of this type of structure is the degradation of the lateral cooperative capability, that is, the deterioration of the hinge joint stiffness. This paper proposes a monitoring and evaluation method for the lateral connection stiffness based on the multiple-beams theory, which has the concise physical meanings and can be easily implemented. On this basis, a new damage identification method and index extracted from the mode shape information of the system is proposed in this paper. To verify the effectiveness of the proposed indicator ARCR, Shanghai Tongji Road viaduct is taken as the engineering background. Results shows that the theoretical analysis results are consistent with physical truth, the proposed indicator can reasonably reflect the degradation degree of the joint and locate the damaged joint.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-21T11:18:35Z
      DOI: 10.1177/14759217211047899
       
  • Structural performance assessment considering both observed and latent
           environmental and operational conditions: A Gaussian process and
           probability principal component analysis method

    • Free pre-print version: Loading...

      Authors: Yi-Chen Zhu, Wen Xiong, Xiao-Dong Song
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural faults like damage and degradations will cause changes in structure response data. Performance assessment can be conducted by investigating such changes. In real implementations however, structural responses are affected by environmental and operational variations (EOVs) as well. Such variation should be well captured by the assessment model when detecting structural changes. It should be noted that not all EOVs can be measured by the monitoring system. When both observed and latent EOVs have significant effects on the monitored structural responses, these two effects should be considered properly. Furthermore, uncertainties can be significant for the monitoring data since loads and EOVs cannot be directly controlled under working conditions. To address these problems, this work proposes a performance assessment method considering both observed and latent EOVs. A Gaussian process is used to model the functional behaviour between structural response and observed EOVs whilst principal component analysis is used to eliminate the effect of latent EOVs. These two methods are combined using a Bayesian formulation and the effect of both observed and latent EOVs are modelled. The associated model parameters are inferred through probability density functions to account for the uncertainties. A synthetic data example is presented to validate the proposed method. It is also applied to the monitoring data of a long-span cable-stayed bridge with different damage scenarios considered, illustrating its capability of real implementations in structural health monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-13T05:32:37Z
      DOI: 10.1177/14759217211062099
       
  • A two-step computer vision-based framework for bolt loosening detection
           and its implementation on a smartphone application

    • Free pre-print version: Loading...

      Authors: Yanzhi Qi, Peizhen Li, Bing Xiong, Shuyin Wang, Cheng Yuan, Qingzhao Kong
      Abstract: Structural Health Monitoring, Ahead of Print.
      Bolt loosening detection is a labor-intensive and time-consuming process for field engineers. This paper develops a two-step computer vision-based framework to quickly identify bolt loosening angle from field images captured by unmanned aerial vehicle (UAV). In step one, a total of 1200 image samples of bolted structures were used to train faster region based convolutional neural network (Faster R-CNN) for bolt detection from UAV captured images. In step two, computer vision-based technologies, including Gaussian filter, perspective transform, and Hough transform (HT), were performed to quantify bolt loosening angle. The developed framework was then integrated into web server and an iOS application (app) was designed to enable fast data communication between field workplace (UAV captured images) and web server (bolt loosening angle quantification), so that field engineers can quickly view the inspection results on their phone screens. The proposed framework and designed smartphone app greatly help field engineers to improve the accuracy and efficiency for onsite inspection and maintenance of bolted structures.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-11T12:02:30Z
      DOI: 10.1177/14759217211049995
       
  • Diagnosis of internal cracks in concrete using vibro-acoustic modulation
           and machine learning

    • Free pre-print version: Loading...

      Authors: Sarah Miele, Pranav M Karve, Sankaran Mahadevan, Vivek Agarwal
      Abstract: Structural Health Monitoring, Ahead of Print.
      This paper investigates the utility of physics-informed machine learning models for vibro-acoustic modulation (VAM)–based damage localization in concrete structures. Vibro-acoustic modulation is a nonlinear dynamics-based non-destructive testing method, which was initially developed to perform damage detection and later extended to accomplish damage localization. The VAM-based damage (hidden crack) diagnosis is performed by analyzing the damage index pattern on the surface of the component to arrive at the size and location of the hidden damage. Past investigations have employed heuristically selected damage index thresholds as well as computationally expensive Bayesian estimation methods for VAM-based damage localization in two (surface) dimensions. Compared to these studies, the proposed methodology automates the threshold selection (algorithmic instead of heuristic), increases the speed of the probabilistic damage diagnosis process, and enables the estimation of damage depth. We generate training data (damage index) for the machine learning models using the pertinent nonlinear dynamics (finite element) models using different combinations of test parameters. The (supervised) machine learning models are thus informed by computational physics models. These include two types of artificial neural network (ANN) models: classification models that identify whether a sensor location is damaged or not and regression models that enable Bayesian estimation to obtain the posterior probability distribution of damage location and size. The accuracy of machine learning-based diagnosis is evaluated using both numerical and laboratory experiments. The proposed physics-informed machine learning models for VAM-based damage diagnosis are able to achieve an accuracy of about 60–64% in the validation experiments, indicating the potential of these methods for internal crack detection. The results show that for complex (nonlinear dynamics-driven) diagnostic methods, damage index patterns learned from physics models could be successfully used for damage detection as well as localization.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-10T10:03:42Z
      DOI: 10.1177/14759217211047901
       
  • Crack detection of concrete structures using deep convolutional neural
           networks optimized by enhanced chicken swarm algorithm

    • Free pre-print version: Loading...

      Authors: Yang Yu, Maria Rashidi, Bijan Samali, Masoud Mohammadi, Thuc N Nguyen, Xinxiu Zhou
      Abstract: Structural Health Monitoring, Ahead of Print.
      With the rapid increase of ageing infrastructures worldwide, effective and robust inspection techniques are highly demanding to evaluate structural conditions and residual lifetime. The damages on structural surfaces, for example, spalling, crack, rebar buckling and exposure, are important indicators to assess the structural condition. In fact, several state-of-the-art automated inspection techniques using these indicators have been developed to reduce human-conducted onsite inspection activities. However, the efficiency of these techniques is still required to be improved in terms of accuracy and computational cost. In this study, a vision-based crack diagnosis method is developed using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA). A DCNN model is designed with a deep architecture, consisting of six convolutional layers, two pooling layers and three fully connected layers. To enhance the generalisation capacity of trained model, ECSA is introduced to optimize meta-parameters of the DCNN model. The model is trained and tested using image patches cropped from raw images obtained from damaged concrete samples. Finally, a comparative study on different crack detection techniques is conducted to evaluate performance of the proposed method via a group of statistical evaluation indicators.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-09T06:18:35Z
      DOI: 10.1177/14759217211053546
       
  • Damage detection in cementitious materials with optimized absolute
           electrical resistance tomography

    • Free pre-print version: Loading...

      Authors: Xiaoyong Zhou, Jiahui Wang, Fubin Tu, Prakash Bhat
      Abstract: Structural Health Monitoring, Ahead of Print.
      Electrical resistance tomography (ERT) serves as a non-invasive, non-destructive, non-radioactive imaging technique. It has potential applications in industrial and biological imaging. This paper presents an optimized inverse algorithm, named Newton’s Constrained Reconstruction Method (NCRM), to detect damage in cementitious materials. Several constraints were utilized in the proposed algorithm to optimize initial parameters. The range and spatial distribution of conductivities within the sample were chosen as two main constraints. Two sets of numerical and a set of experimental voltage data were used to reconstruct conductivity distribution images based on this algorithm. To evaluate the quality of reconstructed images, two image quality evaluation indicators, correlation coefficient and position error were used. Results show that the proposed algorithm NCRM has the ability to enhance the reconstructed image quality with fewer artifacts and has better positioning accuracy.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-04T08:16:01Z
      DOI: 10.1177/14759217211059066
       
  • Sensor data-based probabilistic monitoring of time-history deflections of
           railway bridges induced by high-speed trains

    • Free pre-print version: Loading...

      Authors: Jaebeom Lee, Seunghoo Jeong, Junhwa Lee, Sung-Han Sim, Kyoung-Chan Lee, Young-Joo Lee
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-03T12:06:00Z
      DOI: 10.1177/14759217211063424
       
  • Deep learning enhanced principal component analysis for structural health
           monitoring

    • Free pre-print version: Loading...

      Authors: Ana Fernandez-Navamuel, Filipe Magalhães, Diego Zamora-Sánchez, Ángel J Omella, David Garcia-Sanchez, David Pardo
      First page: 1710
      Abstract: Structural Health Monitoring, Ahead of Print.
      This paper proposes a Deep Learning Enhanced Principal Component Analysis (PCA) approach for outlier detection to assess the structural condition of bridges. We employ partially explainable autoencoder architecture to replicate and enhance the data compression and reconstruction ability of PCA. The particularity of the method lies in the addition of residual connections to account for nonlinearities. We apply the proposed method to monitoring data obtained from two bridges under real operation conditions and compare the results before and after adding the residual connections. Results show that the addition of residual connections enhances the outlier detection ability of the network, allowing to detect lighter damages.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-18T09:20:52Z
      DOI: 10.1177/14759217211041684
       
  • Railway defect detection based on track geometry using supervised and
           unsupervised machine learning

    • Free pre-print version: Loading...

      Authors: Jessada Sresakoolchai, Sakdirat Kaewunruen
      First page: 1757
      Abstract: Structural Health Monitoring, Ahead of Print.
      Track quality affects passenger comfort and safety. To maintain the quality of the track, track geometry and track component defects are inspected routinely. Track geometry is inspected using a track geometry car (TGC). Measured values are stored in the machine and processed to evaluate the track quality. However, track component defects require more effort to inspect. Track component defects can be inspected manually which is time- and workload-consuming or using sensors installed at additional cost. This study presents an approach using track geometry obtained by a TGC to detect track component defects, namely, rail, switch and crossing, fastener and rail joint defects. Detection models are developed using several supervised machine learnings. The relationships between track component defects are analysed to gain insights using unsupervised machine learnings. From the study, the best model for detecting track component defects using track geometry is a deep neural network with an accuracy of 94.31% followed by a convolutional neural network with an accuracy of 93.77%. For the exploration of insights, k-means clustering is used to cluster the track components defects, and association rules are used to find the relationships between them. Examples of the insights from applying these two techniques are that switch and crossing defects are usually found where the radius of curvature is less than 2000 m and the gradient is positive, the most common defects when the radius of curvature higher than 4000 m are rail defects, or a worn wing rail will be found when the rail section has failed, ties in switches and worn point blades are found with the confidence of 92.17%. The findings of the study can be applied to detect track component defects using track geometry where additional cost is not required and unsupervised machine learning provides the insights that will be beneficial for railway maintenance. The information obtained from machine learning models will be complementary information to support decision making and improve the maintenance efficiency in the railway industry.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-29T09:17:13Z
      DOI: 10.1177/14759217211044492
       
  • Monitoring deformations of infrastructure networks: A fully automated GIS
           integration and analysis of InSAR time-series

    • Free pre-print version: Loading...

      Authors: Valentina Macchiarulo, Pietro Milillo, Chris Blenkinsopp, Giorgia Giardina
      First page: 1849
      Abstract: Structural Health Monitoring, Ahead of Print.
      Ageing stock and extreme weather events pose a threat to the safety of infrastructure networks. In most countries, funding allocated to infrastructure management is insufficient to perform systematic inspections over large transport networks. As a result, early signs of distress can develop unnoticed, potentially leading to catastrophic structural failures. Over the past 20 years, a wealth of literature has demonstrated the capability of satellite-based Synthetic Aperture Radar Interferometry (InSAR) to accurately detect surface deformations of different types of assets. Thanks to the high accuracy and spatial density of measurements, and a short revisit time, space-borne remote-sensing techniques have the potential to provide a cost-effective and near real-time monitoring tool. Whilst InSAR techniques offer an effective approach for structural health monitoring, they also provide a large amount of data. For civil engineering procedures, these need to be analysed in combination with large infrastructure inventories. Over a regional scale, the manual extraction of InSAR-derived displacements from individual assets is extremely time-consuming and an automated integration of the two datasets is essential to effectively assess infrastructure systems. This paper presents a new methodology based on the fully automated integration of InSAR-based measurements and Geographic Information System-infrastructure inventories to detect potential warnings over extensive transport networks. A Sentinel dataset from 2016 to 2019 is used to analyse the Los Angeles highway and freeway network, while the Italian motorway network is evaluated by using open access ERS/Envisat datasets between 1992 and 2010, COSMO-SkyMed datasets between 2008 and 2014 and Sentinel datasets between 2014 and 2020. To demonstrate the flexibility of the proposed methodology to different SAR sensors and infrastructure classes, the analysis of bridges and viaducts in the two test areas is also performed. The outcomes highlight the potential of the proposed methodology to be integrated into structural health monitoring systems and improve current procedures for transport network management.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-06T04:34:02Z
      DOI: 10.1177/14759217211045912
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.236.221.156
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-