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World Journal of Engineering     Full-text available via subscription   (Followers: 3)
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Вісник Приазовського Державного Технічного Університету. Серія: Технічні науки     Open Access  

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Journal Cover Journal of Quality in Maintenance Engineering
  [SJR: 0.503]   [H-I: 37]   [5 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1355-2511
   Published by Emerald Homepage  [335 journals]
  • Guest editorial
    • Pages: 258 - 259
      Abstract: Journal of Quality in Maintenance Engineering, Volume 23, Issue 3, Page 258-259, August 2017.

      Citation: Journal of Quality in Maintenance Engineering
      PubDate: 2017-08-24T07:23:45Z
      DOI: 10.1108/JQME-05-2017-0039
  • Dealing with missing data as it pertains of e-maintenance
    • Pages: 260 - 278
      Abstract: Journal of Quality in Maintenance Engineering, Volume 23, Issue 3, Page 260-278, August 2017.
      Purpose Centrifugal compressors are integral components in oil industry, thus effective maintenance is required. Condition-based maintenance and prognostics and health management (CBM/PHM) have been gaining popularity. CBM/PHM can also be performed remotely leading to e-maintenance. Its success depends on the quality of the data used for analysis and decision making. A major issue associated with it is the missing data. Their presence may compromise the information within a set, causing bias or misleading results. Addressing this matter is crucial. The purpose of this paper is to review and compare the most widely used imputation techniques in a case study using condition monitoring measurements from an operational industrial centrifugal compressor. Design/methodology/approach Brief overview and comparison of most widely used imputation techniques using a complete set with artificial missing values. They were tested regarding the effects of the amount, the location within the set and the variable containing the missing values. Findings Univariate and multivariate imputation techniques were compared, with the latter offering the smallest error levels. They seemed unaffected by the amount or location of the missing data although they were affected by the variable containing them. Research limitations/implications During the analysis, it was assumed that at any time only one variable contained missing data. Further research is still required to address this point. Originality/value This study can serve as a guide for selecting the appropriate imputation method for missing values in centrifugal compressor condition monitoring data.
      Citation: Journal of Quality in Maintenance Engineering
      PubDate: 2017-08-24T07:23:42Z
      DOI: 10.1108/JQME-08-2016-0032
  • Effective vibration-based condition monitoring (eVCM) of rotating machines
    • Pages: 279 - 296
      Abstract: Journal of Quality in Maintenance Engineering, Volume 23, Issue 3, Page 279-296, August 2017.
      Purpose The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework. Design/methodology/approach The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM. Findings This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance. Research limitations/implications The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine. Practical implications The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data. Social implications The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults. Originality/value Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.
      Citation: Journal of Quality in Maintenance Engineering
      PubDate: 2017-08-24T07:23:32Z
      DOI: 10.1108/JQME-08-2016-0036
  • Simulation based study on improving the transient response quality of
           turbocharged diesel engines
    • Pages: 297 - 309
      Abstract: Journal of Quality in Maintenance Engineering, Volume 23, Issue 3, Page 297-309, August 2017.
      Purpose Use of fossil fuels in automotive sector is one of the primary causes of greenhouse emissions. The automotive engines need to perform at their best efficiency point to limit these emissions. Most of the quality indicators in this regard are based on near steady state global operational characteristics for engines without considering local performance. In the present study, extensive numerical simulations have been carried out covering a wide range of steady state and transient operating conditions to quantify interaction of turbocharger with engines through turbo lag phenomena which may cause increased emissions during the load change conditions. Furthermore possible innovations have been explored to minimize turbo lag phenomena. The paper aims to discuss these issues. Design/methodology/approach In this paper quality indicators have been developed to quantify the performance of turbocharged diesel engine under the transient event of rapid change in fueling rate which has been rarely investigated. The rate of fueling is changed from 40 mm3/injection to 52 mm3/injection at 1,000 rpm engine speed which corresponds to normal operating condition. To improve quality of transient response, torque assistance method and reduction of inertia of compressor wheel have been used. Parametric study has been undertaken to analyze the quality indicators such as outlet pressure of the compressor and the compressor speed. The turbo lag is quantified to obtain the close to optimal transient response of turbocharged diesel engine. Findings It has been shown that, with torque assist the transient response of the internal combustion engine is significantly improved. On the other hand, marginal improvement in transient response is observed by the reduction in inertia of the compressor wheel. Research limitations/implications The findings indicate that turbo lag can be minimized by providing torque assistance by active and passive means. Practical implications The developed methods can be used in practice for efficient operation of vehicles. Social implications The work carried out in the paper provides a way to minimize harmful emissions. Originality/value The quality indicators developed provide a quantitative measure of turbo lag phenomena and address the above mentioned problems.
      Citation: Journal of Quality in Maintenance Engineering
      PubDate: 2017-08-24T07:23:30Z
      DOI: 10.1108/JQME-08-2016-0037
  • Maintenance analytics for railway infrastructure decision support
    • Pages: 310 - 325
      Abstract: Journal of Quality in Maintenance Engineering, Volume 23, Issue 3, Page 310-325, August 2017.
      Purpose The purpose of this paper is to present a framework for maintenance analytics that is useful for the assessment of rail condition and for maintenance decision support. The framework covers three essential maintenance aspects: diagnostic, prediction and prescription. The paper also presents principal component analysis (PCA) and local outlier factor methods for detecting anomalous rail wear occurrences using field measurement data. Design/methodology/approach The approach used in this paper includes a review of the concept of analytics and appropriate adaptation to railway infrastructure maintenance. The diagnostics aspect of the proposed framework is demonstrated with a case study using historical rail profile data collected between 2007 and 2016 for nine sharp curves on the heavy haul line in Sweden. Findings The framework presented for maintenance analytics is suitable for extracting useful information from condition data as required for effective rail maintenance decision support. The findings of the case study include: combination of the two statistics from PCA model (T2 and Q) can help to identify systematic and random variations in rail wear pattern that are beyond normal: the visualisation approach is a better tool for anomaly detection as it categorises wear observations into normal, suspicious and anomalous observations. Practical implications A practical implication of this paper is that the framework and the diagnostic tool can be considered as an integral part of e-maintenance solution. It can be easily adapted as online or on-board maintenance analytic tool with data from automated vehicle-based measurement system. Originality/value This research adapts the concept of analytics to railway infrastructure maintenance for enhanced decision making. It proposes a graphical method for combining and visualising different outlier statistics as a reliable anomaly detection tool.
      Citation: Journal of Quality in Maintenance Engineering
      PubDate: 2017-08-24T07:23:26Z
      DOI: 10.1108/JQME-11-2016-0059
  • Systematic risk-analysis to support a living maintenance programme for
           railway infrastructure
    • Pages: 326 - 340
      Abstract: Journal of Quality in Maintenance Engineering, Volume 23, Issue 3, Page 326-340, August 2017.
      Purpose The purpose of this paper is to describe an application of an effective risk-based methodology to support a living maintenance programme for railway infrastructure. Design/methodology/approach The overall research strategy is a single case study of switches and crossings at the Iron Ore Line in northern Sweden. The analysis was performed as a risk workshop guided by a methodology that integrates reliability-centred maintenance and barrier analysis. Findings The applied methodology is valuable to systematise and improve the existing maintenance programme, as well as supporting a continued living maintenance programme. Research limitations/implications The single case study approach may decrease the validity of the achieved results. However, similar case studies corroborate the results, which affect the validity in a positive way. Practical implications The resulting maintenance programme is effective, through compliance with external requirements, and more efficient, through improvements of tasks and intervals. Social implications An enhanced railway infrastructure maintenance programme contributes to improved safety, punctuality, and costs. Hence, railway becomes a more attractive mode of transport. Thereby, it also supports a safety performance of the railway that society is willing to pay for. Originality/value Significant improvements of the maintenance programme are achieved through adjustment of inspection intervals and tasks. The results also support the development of indicators, monitoring, and continuous improvement.
      Citation: Journal of Quality in Maintenance Engineering
      PubDate: 2017-08-24T07:23:27Z
      DOI: 10.1108/JQME-09-2016-0042
  • Context preparation for predictive analytics – a case from
           manufacturing industry
    • Pages: 341 - 354
      Abstract: Journal of Quality in Maintenance Engineering, Volume 23, Issue 3, Page 341-354, August 2017.
      Purpose The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring (CM) measurements data and information related to the context are gathered and analysed. Design/methodology/approach This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new CM function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context-related information have been identified. Findings Multiple sources of relevant contextual information have been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed. Originality/value This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process.
      Citation: Journal of Quality in Maintenance Engineering
      PubDate: 2017-08-24T07:23:47Z
      DOI: 10.1108/JQME-10-2016-0050
  • Industrial internet applications for efficient road winter maintenance
    • Pages: 355 - 367
      Abstract: Journal of Quality in Maintenance Engineering, Volume 23, Issue 3, Page 355-367, August 2017.
      Purpose For the expected increase in the capacity of existing transportation systems and efficient energy utilisation, smart maintenance solutions that are supported by online and integrated condition monitoring systems are required. Industrial internet is one of the smart maintenance solutions which enables real-time acquisition and analysis of asset condition by linking intelligent devices with different stakeholders’ applications and databases. The purpose of this paper is to present some aspects of industrial internet application as required for integrating weather information and floating road condition data from vehicle mounted sensors to enhance effective and efficient winter maintenance. Design/methodology/approach The concept of real-time road condition assessment using in-vehicle sensors is demonstrated in a case study of a 3.5 km road section located in Northern Sweden. The main floating data sources were acceleration and position sensors from a smartphone positioned on the dash board of a truck. Features extracted from the acceleration signal were two road roughness estimations. To extract targeted information and knowledge, the floating data were further processed to produce time series data of the road condition using Kalman filtering. The time series data were thereafter combined with weather data to assess the condition of the road. Findings In the case study, examples of visualisation and analytics to support winter maintenance planning, execution and resource allocation were presented. Reasonable correlation was shown between estimated road roughness and annual road survey data to validate and prove the presented results wider applicability. Originality/value The paper describes a concept of floating data for an industrial internet application for efficient road maintenance. The resulting improvement in winter maintenance will promote dependable, safe and sustainable transportation of goods and people, especially in Northern Nordic region with harsh and sometimes unpredictable weather conditions.
      Citation: Journal of Quality in Maintenance Engineering
      PubDate: 2017-08-24T07:23:31Z
      DOI: 10.1108/JQME-11-2016-0071
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Heriot-Watt University
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