Special Issue "Machinery Condition Monitoring and Industrial Analytics"

A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (28 February 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Nagi Z. Gebraeel

The H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Website | E-Mail
Interests: condition monitoring; prognostics; diagnostics; anomaly detection; condition-based maintenance; degradation modeling; physical degradation; optimization; decision-making
Guest Editor
Prof. Dr. Ricardo Albarracín

Department of Electrical, Electronic and Automation Engineering and Applied Physics, Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politécnica de Madrid, Ronda de Valencia 3, Madrid 28012, Spain
Website | E-Mail
Phone: +34-913366858
Interests: insulation systems diagnosis within power cables and electrical machines; condition monitoring; partial discharges measured inductively and with antennas; location of PD sources; signal processing, identification of PD sources and noise rejection; behaviour of oil-based nanofluids for transformers

Special Issue Information

Dear Colleagues,

Condition monitoring is the process of collecting (sensor) data from industrial machinery/assets to assess their state of health and to ensure reliable operation. The literature in this area has primarily revolved around fault detection, diagnostics, and prognostics. Although condition monitoring has had a long and rich history, most of the modeling approaches have been deeply rooted in custom-built models for specific machine components. Today, growing trends in instrumentation have created several critical challenges related to data analytics that require us to rethink conventional modeling paradigms. The volume and dimensionality of condition monitoring data generated by modern machinery has become prohibitive. However, a great deal of these models are either exclusively physics-based models, which do not leverage data, or are data-driven models that have only been validated using small datasets.

The aim of this Special Issue is to report recent advances that address the following challenges, (1) real-time modeling and analysis of massive amounts of different sensor data, (2) integrating physical degradation models with data-driven modeling approaches, and (3) bridging the gap between condition research and subsequent decision making.

Prof. Dr. Nagi Z. Gebraeel
Prof. Dr. Ricardo Albarracín
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

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Open AccessFeature PaperArticle Development of a Methodology for Condition-Based Maintenance in a Large-Scale Application Field
Received: 27 February 2018 / Revised: 9 April 2018 / Accepted: 10 April 2018 / Published: 16 April 2018
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Abstract
This paper describes a methodology, developed by the authors, for condition monitoring and diagnostics of several critical components in the large-scale applications with machines. For industry, the main target of condition monitoring is to prevent the machine stopping suddenly and thus avoid economic
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This paper describes a methodology, developed by the authors, for condition monitoring and diagnostics of several critical components in the large-scale applications with machines. For industry, the main target of condition monitoring is to prevent the machine stopping suddenly and thus avoid economic losses due to lack of production. Once the target is reached at a local level, usually through an R&D project, the extension to a large-scale market gives rise to new goals, such as low computational costs for analysis, easily interpretable results by local technicians, collection of data from worldwide machine installations, and the development of historical datasets to improve methodology, etc. This paper details an approach to condition monitoring, developed together with a multinational corporation, that covers all the critical points mentioned above. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Open AccessFeature PaperArticle A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements
Received: 22 December 2017 / Revised: 2 January 2018 / Accepted: 4 January 2018 / Published: 5 January 2018
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Abstract
For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using
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For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using sensors are often related to the costs associated with them and the inaccessibility of critical locations within machines or processes. Manufacturing processes, as a large group of applications in which data analytics can bring significant value to, are the focus of this study. As the cost of instrumenting the machines in a manufacturing process is significant, an alternative solution which relies solely on product quality measurements is greatly desirable in the manufacturing industry. In this paper, a minimal-sensing framework for machine anomaly detection in multistage manufacturing processes based on product quality measurements is introduced. This framework, which relies on product quality data along with products’ manufacturing routes, allows the detection of variations in the quality of the products and is able to pinpoint the machine which is the cause of anomaly. A moving window is applied to the data, and a statistical metric is extracted by comparing the performance of a machine to its peers. This approach is expanded to work for multistage processes. The proposed method is validated using a dataset from a real-world manufacturing process and additional simulated datasets. Moreover, an alternative approach based on Bayesian Networks is provided and the performance of the two proposed methods is evaluated from an industrial implementation perspective. The results showed that the proposed similarity-based approach was able to successfully identify the root cause of the quality variations and pinpoint the machine that adversely impacted the product quality. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Open AccessArticle The Decision making System for Condition Monitoring of Induction Motors Based on Vector Control Model
Received: 23 October 2017 / Revised: 7 November 2017 / Accepted: 9 November 2017 / Published: 12 November 2017
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Abstract
Induction motors are mainly used for variable load applications and it is vital to have a condition monitoring system with capabilities to diagnose motor faults in variable load conditions. The environment noise varies has non-linear relation with motor load and it challenges the
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Induction motors are mainly used for variable load applications and it is vital to have a condition monitoring system with capabilities to diagnose motor faults in variable load conditions. The environment noise varies has non-linear relation with motor load and it challenges the decision making capability of the condition monitoring system. This paper addresses the issue of reliable decision making on the existence of bearing faults in variable load conditions. Two type of threshold schemes have been proposed to reliably diagnose bearing faults in Park vector modulus spectrum. The performance of the developed threshold based condition monitoring system has been analyzed theoretically and experimentally. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Open AccessArticle Drilling Rig Hoisting Platform Security Monitoring System Design and Application
Received: 1 June 2017 / Revised: 2 August 2017 / Accepted: 3 August 2017 / Published: 22 August 2017
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Abstract
Drilling rig hoisting platform security monitoring system has played a very important role in oil exploration. And drilling parameters and working condition of workers are particularly important, because these parameters indicate that whether the drilling work is safe and effective directly. A security
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Drilling rig hoisting platform security monitoring system has played a very important role in oil exploration. And drilling parameters and working condition of workers are particularly important, because these parameters indicate that whether the drilling work is safe and effective directly. A security monitoring system is established to provide the real-time parameters for drilling safety is the purpose of this study. The monitoring system includes a top drive, a traveling block hook, an oil derrick and a driller room, and the controller of the system is programmable logic controller PLC. The procedure of the system is written by the RSLogix5000 software, the PC configuration is used force control monitor configuration software. According to the system, top drive, driller room and environment wind speed parameters in real-time are collected and displayed in the configuration of upper computer, the collected parameters can be used to determine the working conditions of the top drive and to send timely warnings for inspection maintenance to avoid drilling safety accidents. Work fatigue remind of driller and regularly remind of derrick check can be as much as possible to reduce safety accidents. And automatic operation of traveling block hook reached the default point faster and more smoothly than manual operation given the other uncontrollable factors in manual operation. The application of the system is successfully working in the drilling work site. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Open AccessArticle An Ensemble-Boosting Algorithm for Classifying Partial Discharge Defects in Electrical Assets
Received: 18 July 2017 / Revised: 3 August 2017 / Accepted: 4 August 2017 / Published: 8 August 2017
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Abstract
This paper presents an ensemble-boosting algorithm (EBA) for classifying partial discharge (PD) patterns in the condition monitoring of insulation diagnosis applied for electrical assets. This approach presents an optimization technique for creating a sequence of artificial neural network (ANNs), where the training data
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This paper presents an ensemble-boosting algorithm (EBA) for classifying partial discharge (PD) patterns in the condition monitoring of insulation diagnosis applied for electrical assets. This approach presents an optimization technique for creating a sequence of artificial neural network (ANNs), where the training data for each constituent of the sequence is selected based on the performance of previous ANNs. Four different PD faults scenarios were manufactured in the high-voltage (HV) laboratory to simulate the PD faults of cylindrical voids in methacrylate, point-air-plane configuration, ceramic bushing with contaminated surface and a transformer affected by the internal PD. A PD dataset was collected, pre-processed and prepared for its use in the improved boosting algorithm using statistical techniques. In this paper, the EBA is extensively compared with the widely used single artificial neural network (SNN). Results show that the proposed approach can effectively improve the generalization capability of the PD patterns. The application of the proposed technique for both online and offline practical PD recognition is examined. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Review

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Open AccessReview Rotating Electrical Machine Condition Monitoring Automation—A Review
Received: 25 August 2017 / Revised: 3 October 2017 / Accepted: 13 October 2017 / Published: 19 October 2017
Cited by 1 | PDF Full-text (1110 KB) | HTML Full-text | XML Full-text
Abstract
We review existing machine condition monitoring techniques and industrial automation for plant-wide condition monitoring of rotating electrical machines. Cost and complexity of a condition monitoring system increase with the number of measurements, so extensive condition monitoring is currently mainly restricted to the situations
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We review existing machine condition monitoring techniques and industrial automation for plant-wide condition monitoring of rotating electrical machines. Cost and complexity of a condition monitoring system increase with the number of measurements, so extensive condition monitoring is currently mainly restricted to the situations where the consequences of poor availability, yield or quality are so severe that they clearly justify the investment in monitoring. There are challenges to obtaining plant-wide monitoring that includes even small machines and non-critical applications. One of the major inhibiting factors is the ratio of condition monitoring cost to equipment cost, which is crucial to the acceptance of using monitoring to guide maintenance for a large fleet of electrical machinery. Ongoing developments in sensing, communication and computation for industrial automation may greatly extend the set of machines for which extensive monitoring is viable. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Open AccessReview A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing
Received: 9 August 2017 / Revised: 16 September 2017 / Accepted: 19 September 2017 / Published: 27 September 2017
Cited by 3 | PDF Full-text (5781 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed
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This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed (≈ 1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. The selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure. Full article
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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