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Machines
Special Issues
Condition Monitoring and Fault Diagnosis of Induction Motors
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Condition Monitoring and Fault Diagnosis of Induction Motors

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 6309

Special Issue Editors

Dr. Ioannis D. Chasiotis

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Guest Editor
Electrical Machines Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, University Campus, Kimmeria, GR-671 00 Xanthi, Greece
Interests: modeling, design and thermal analysis of electrical machines; analysis and design of integrated direct-drive systems for traction and propulsion applications; parameters estimation and fault diagnosis of induction motors; artificial intelligence methods application to electrical machines
Prof. Dr. Yannis L. Karnavas

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Guest Editor
Electrical Machines Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, University Campus, GR-671 00 Xanthi, Greece
Interests: electrical machines design; analysis, modeling, optimization and fault diagnosis of electrical machines; controller design; artificial intelligence methods application to electrical machines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Induction motors (IMs) are critical components in numerous industrial applications. Although they require only basic maintenance, unexpected breakdown and failures may occur due to their continuous operation under harsh conditions. The occurrence of faults can lead to a production shutdown, which may result in great economic losses. To avoid the above consequences, it is essential to develop robust mechanisms that permit: i) the regular condition monitoring of industrial drives, ii) the fast and reliable assessment of their health status and iii) the accurate determination of the fault severity. This allows the prognosis of early developing faults, which minimizes unscheduled downtime, improves productivity and reduces maintenance costs. The condition-based maintenance is also aligned with the context of Industry 4.0 and has attracted widespread attention. The goal of this Special Issue is to bring researchers together to share their research findings and present attractive perspectives in the fields of: a) online monitoring technologies for industrial drives, b) IMs fault detection, diagnosis and prognosis, c) analysis and processing of obtained information, d) IMs remaining useful life prediction and e) scientific support towards the establishment of new technical standards regarding machinery condition monitoring and predictive maintenance. This Special Issue encourages and welcomes both original research articles (with significant contribution to numerical, theoretical and/or experimental analysis) and review articles related to the aforementioned application areas. Topics of interest include but are not limited to the following:

  • Predictive maintenance and real-time condition monitoring systems;
  • On-line and remote location monitoring technologies;
  • Non-invasive techniques for data acquisition;
  • Robust methodologies for measurement, testing and diagnostics;
  • Modern methods for signal processing;
  • Enhanced pattern recognition algorithms;
  • Failure mechanism analysis;
  • Induction motors’ remaining useful life prediction;
  • Diagnostic approaches for all types of faults (i.e., mechanical, electrical, electromechanical, incipient, multiple simultaneous faults, etc.);
  • Advanced artificial intelligence based fault diagnosis methods;

Dr. Ioannis D. Chasiotis
Prof. Dr. Yannis L. Karnavas
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 submissions that pass pre-check are 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 monthly 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 2400 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.

Keywords

  • induction motors
  • fault detection, diagnosis and prognosis
  • predictive maintenance
  • operating condition monitoring
  • real-time monitoring technologies
  • signal processing and analysis
  • machine learning and artificial intelligence

Published Papers (4 papers)

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Research

29 pages, 11412 KiB  
Article
A Study of Noise Effect in Electrical Machines Bearing Fault Detection and Diagnosis Considering Different Representative Feature Models
by Dimitrios A. Moysidis, Georgios D. Karatzinis, Yiannis S. Boutalis and Yannis L. Karnavas
Machines 2023, 11(11), 1029; https://doi.org/10.3390/machines11111029 - 17 Nov 2023
Cited by 1 | Viewed by 1292
Abstract
As the field of fault diagnosis in electrical machines has significantly attracted the interest of the research community in recent years, several methods have arisen in the literature. Also, raw data signals can be acquired easily nowadays, and, thus, machine learning (ML) and [...] Read more.
As the field of fault diagnosis in electrical machines has significantly attracted the interest of the research community in recent years, several methods have arisen in the literature. Also, raw data signals can be acquired easily nowadays, and, thus, machine learning (ML) and deep learning (DL) are candidate tools for effective diagnosis. At the same time, a challenging task is to identify the presence and type of a bearing fault under noisy conditions, especially when relevant faults are at their incipient stage. Since, in real-world applications and especially in industrial processes, electrical machines operate in constantly noisy environments, a key to an effective approach lies in the preprocessing stage adopted. In this work, an evaluation study is conducted to find the most suitable signal preprocessing techniques and the most effective model for fault diagnosis of 16 conditions/classes, from a low-workload (computational burden) perspective using a well-known dataset. More specifically, the reliability and resiliency of conventional ML and DL models is investigated here, towards rolling bearing fault detection, simulating data that correspond to noisy industrial environments. Diverse preprocessing methods are applied in order to study the performance of different training methods from the feature extraction perspective. These feature extraction methods include statistical features in time-domain analysis (TDA); wavelet packet decomposition (WPD); continuous wavelet transform (CWT); and signal-to-image conversion (SIC), utilizing raw vibration signals acquired under varying load conditions. The noise effect is examined and thoroughly commented on. Finally, the paper provides accumulated usual practices in the sense of preferred preprocessing methods and training models under different load and noise conditions. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis of Induction Motors)
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23 pages, 5139 KiB  
Article
Detection of Inter-Turn Short Circuits in Induction Motors under the Start-Up Transient by Means of an Empirical Wavelet Transform and Self-Organizing Map
by Juan Jose Saucedo-Dorantes, Arturo Yosimar Jaen-Cuellar, Angel Perez-Cruz and David Alejandro Elvira-Ortiz
Machines 2023, 11(10), 958; https://doi.org/10.3390/machines11100958 - 14 Oct 2023
Viewed by 1043
Abstract
Due to the importance of induction motors in a wide variety of industrial processes, it is crucial to properly identify abnormal conditions in order to avoid unexpected stops. The inter-turn short circuit (ITSC) is a very common failure produced with electrical stresses and [...] Read more.
Due to the importance of induction motors in a wide variety of industrial processes, it is crucial to properly identify abnormal conditions in order to avoid unexpected stops. The inter-turn short circuit (ITSC) is a very common failure produced with electrical stresses and affects induction motors (IMs), leading to catastrophic damage. Therefore, this work proposes the use of the empirical wavelet transform to characterize the time frequency behavior of the IM combined with a self-organizing map (SOM) structure to perform an automatic detection and classification of different severities of ITSC. Since the amount of information obtained from the empirical wavelet transform is big, a genetic algorithm is implemented to select the modes that allow a reduction in the quantization error in the SOM. The proposed methodology is applied to a real IM during the start-up transient considering four different fundamental frequencies. The results prove that this technique is able to detect and classify three different fault severities regardless of the operation frequency. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis of Induction Motors)
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18 pages, 7624 KiB  
Article
Stator Faults Detection in Asymmetrical Six-Phase Induction Motor Drives with Single and Dual Isolated Neutral Point, Adopting a Model Predictive Controller
by Khaled Laadjal, João Serra and Antonio J. Marques Cardoso
Machines 2023, 11(2), 132; https://doi.org/10.3390/machines11020132 - 18 Jan 2023
Cited by 2 | Viewed by 1535
Abstract
Multiphase drives have been presented as potential replacements for conventional three-phase machines, primarily because of their propensity to operate faultlessly. Due to the various stator phase arrangements, standard fault detection techniques are insufficiently applicable and cannot be used to diagnose faults in the [...] Read more.
Multiphase drives have been presented as potential replacements for conventional three-phase machines, primarily because of their propensity to operate faultlessly. Due to the various stator phase arrangements, standard fault detection techniques are insufficiently applicable and cannot be used to diagnose faults in the various configurations of multiphase machines in closed-loop applications. The current study proposes an effective online diagnostic technique based on the computing and tracking of a significant severity factor, which is defined as the ratio of the zero, negative, and positive voltage symmetrical components employing a short-time least-square Prony algorithm (STLSP). In this study, the asymmetrical six-phase induction motor (ASPIM) was controlled by a model predictive control (MPC) algorithm, an attractive control scheme for the regulation of multiphase electric drives, since it easily exploits their inherent advantages. This article addresses stator faults in ASPIMs. The effectiveness of the suggested strategy was confirmed experimentally for various operating conditions in both steady and transient states. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis of Induction Motors)
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16 pages, 4966 KiB  
Article
A Fault Tolerance Method for Multiple Current Sensor Offset Faults in Grid-Connected Inverters
by Fan Zhang, Guangfeng Jin, Junchao Geng, Tianzhen Wang, Jingang Han, Hubert Razik and Yide Wang
Machines 2023, 11(1), 61; https://doi.org/10.3390/machines11010061 - 04 Jan 2023
Viewed by 1298
Abstract
Three-phase grid-connected inverters have been widely used in the distributed generation system, and the current sensor has been applied in closed-loop control in inverters. When the current sensor offset faults occurs, partial fault features of multiple current sensors disappear from the closed-loop control [...] Read more.
Three-phase grid-connected inverters have been widely used in the distributed generation system, and the current sensor has been applied in closed-loop control in inverters. When the current sensor offset faults occurs, partial fault features of multiple current sensors disappear from the closed-loop control grid-connected system, which leads to difficulties for fault diagnostics and fault-tolerant control. This paper proposes a fault tolerance method based on average current compensation mode to eliminate these adverse effects of fault features. The average current compensation mode compensates the average of the three-phase current to the αβ axis current to realize the fault feature reconstruction of the current sensor. The mode does not affect the normal condition of the system. Then, the data-driven method is used for fault diagnosis, and the corresponding fault tolerant control model is selected according to the diagnosis results. Finally, the experimental results show that the proposed strategy has a good fault tolerance control performance and can improve the fault feature discrimination and diagnostic accuracy. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Diagnosis of Induction Motors)
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