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Electronics
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Machine Fault Detection and Fault-Tolerant Control
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Machine Fault Detection and Fault-Tolerant Control

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (1 November 2022) | Viewed by 21102

Special Issue Editors

Dr. Ignacio González-Prieto

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Málaga, 29016 Málaga, Spain
Interests: multiphase electric drives; model predictive control; fault-tolerant control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mario Duran

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Málaga, 29071 Málaga, Spain
Interests: multiphase electric drives; model predictive control; wind energy conversion systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electric drives currently play an important role in transport thanks to the promotion of clean energies and electric vehicles. The requirements for these emerging applications are also becoming more restrictive. For instance, fault tolerance and continuous operation of drives are now required to increase their reliability. Post-fault operation is especially critical in applications where security is a main concern (e.g., electric vehicle propulsion systems), but it is also appreciated when the shut-down of the electric machine involves a significant economic impact (e.g., wind energy conversion systems).

Based on these new requirements, different research groups have focused on the development of fault-tolerant electric drives. This feature should ideally be obtained without extra hardware, and from this point of view, multiphase machines have an important advantage over conventional three-phase systems due to their inherent redundancy.Despite their better fault tolerance, three software stages are necessary for suitable fault situation management: fault localization and isolation, post-fault control reconfiguration, and derating to safeguard the integrity of the system.

The main objective of this Special Issue of Electronics is to show new advances and developments in machine fault detection and fault-tolerant control to the scientific community and industry.

Dr. Ignacio González-Prieto
Prof. Dr. Mario Duran
Guest Editor

Manuscript Submission Information

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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. Electronics is an international peer-reviewed open access semimonthly 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

  • fault tolerance
  • fault detection
  • fault localization
  • fault diagnosis
  • fault isolation
  • post-fault derating
  • post-fault control
  • multiphase machines
  • electric vehicles
  • wind energy conversion systems

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Published Papers (4 papers)

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Research

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19 pages, 6709 KiB  
Article
CNN-Based Feature Fusion Motor Fault Diagnosis
by Long Qian, Binbin Li and Lijuan Chen
Electronics 2022, 11(17), 2746; https://doi.org/10.3390/electronics11172746 - 31 Aug 2022
Cited by 8 | Viewed by 2361
Abstract
Artificial intelligence fields have been using deep learning in recent years. Due to its powerful data mining capabilities, deep learning has a wide-ranging impact on the diagnosis of motor faults. A method for diagnosing motor faults based on the multi-feature fusion of convolutional [...] Read more.
Artificial intelligence fields have been using deep learning in recent years. Due to its powerful data mining capabilities, deep learning has a wide-ranging impact on the diagnosis of motor faults. A method for diagnosing motor faults based on the multi-feature fusion of convolutional neural network (CNN) is presented in this paper. As far as the method is concerned, CNN is used as the basic framework, and the CNN model has been improved. First, the collected vibration and current signals are preprocessed. Second, segmented multi-time window synchronous input is performed on the processed data. In addition, a multi-scale feature extraction process and time series fusion of vibration and current signals subject to synchronous input in the same time window can be performed, which ultimately enables the identification of motor faults with a high degree of accuracy. In order to verify the validity of the proposed fault diagnosis model, an experimental platform for fault simulation was built for the motor, and vibration and current signals of different motor states were collected and verified by experimentation. According to the results of the experiment, the method can effectively combine motor vibration and current signal fault features, and thus motor fault diagnosis can be improved. In comparison with a single signal input, a multi-signal input provides greater accuracy and stability. As compared to other multi-signal feature fusion methods, such a deep learning model is able to extract fault features in a more comprehensive manner, which helps to improve the accuracy of motor fault diagnosis. Full article
(This article belongs to the Special Issue Machine Fault Detection and Fault-Tolerant Control)
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13 pages, 1714 KiB  
Article
An Adaptive Modeling Framework for Bearing Failure Prediction
by Yuntian Zhao, Maxwell Toothman, James Moyne and Kira Barton
Electronics 2022, 11(2), 257; https://doi.org/10.3390/electronics11020257 - 14 Jan 2022
Cited by 9 | Viewed by 2324
Abstract
Rolling element bearings are a common component in rotating equipment, a class of machines that is essential in a wide range of industries. Detecting and predicting bearing failures is then vital for reducing maintenance and production costs due to unplanned downtime. In previous [...] Read more.
Rolling element bearings are a common component in rotating equipment, a class of machines that is essential in a wide range of industries. Detecting and predicting bearing failures is then vital for reducing maintenance and production costs due to unplanned downtime. In previous literature, significant efforts have been devoted to building data-driven health models from historical bearing data. However, a common limitation is that these methods are typically tailored to specific failure instances and have limited ability to model bearing failures between repairs in the same system. In this paper, we propose a multi-state health model to predict bearing failures before they occur. The model employs a regression-based method to detect health state transition points and applies an exponential random coefficient model with a Bayesian updating process to estimate time-to-failure distributions. A model training framework is also introduced to make our proposed model applicable to more bearing instances in the same system setting. The proposed method has been tested on a publicly available bearing prognostics dataset. Case study results show that the proposed method provides accurate failure predictions across several system failures, and that the training approach can significantly reduce the time necessary to generate an effective, generalized model. Full article
(This article belongs to the Special Issue Machine Fault Detection and Fault-Tolerant Control)
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11 pages, 2416 KiB  
Article
Automatic Fault-Tolerant Control of Multiphase Induction Machines: A Game Changer
by Angel Gonzalez-Prieto, Juan J. Aciego, Ignacio Gonzalez-Prieto and Mario J. Duran
Electronics 2020, 9(6), 938; https://doi.org/10.3390/electronics9060938 - 4 Jun 2020
Cited by 15 | Viewed by 2790
Abstract
Until very recently, the fault tolerance in multiphase electric drives could only be achieved after fault localization and a subsequent modification of the control scheme. This scenario was profoundly shaken with the appearance of the natural fault tolerance, as the control reconfiguration was [...] Read more.
Until very recently, the fault tolerance in multiphase electric drives could only be achieved after fault localization and a subsequent modification of the control scheme. This scenario was profoundly shaken with the appearance of the natural fault tolerance, as the control reconfiguration was not required anymore. Even though the control strategy was highly simplified, it was still necessary to detect the open-phase fault (OPF) in order to derate the electric drive and safeguard its integrity. This work goes one step beyond and suggests the use of an automatic fault-tolerant control (AFTC) that also avoids the detection of the OPF. The AFTC combines the natural fault-tolerant capability with a self-derating technique, finally obtaining a hardware-free software-free fault tolerance. This achievement changes completely the rules of the game in the design of fault-tolerant drives, easing at the same time their industrial application. Experimental results confirm in a six-phase induction motor (IM) drive that the proposed AFTC provides a simple and safe manner to add further reliability to multiphase electric drives. Full article
(This article belongs to the Special Issue Machine Fault Detection and Fault-Tolerant Control)
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Review

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24 pages, 1748 KiB  
Review
A Survey on Active Fault-Tolerant Control Systems
by Alireza Abbaspour, Sohrab Mokhtari, Arman Sargolzaei and Kang K. Yen
Electronics 2020, 9(9), 1513; https://doi.org/10.3390/electronics9091513 - 15 Sep 2020
Cited by 108 | Viewed by 12373
Abstract
Faults and failures in the system components are two main reasons for the instability and the degradation in control performance. In recent decades, fault-tolerant control (FTC) approaches have been introduced to improve the resiliency of control systems against faults and failures. In general, [...] Read more.
Faults and failures in the system components are two main reasons for the instability and the degradation in control performance. In recent decades, fault-tolerant control (FTC) approaches have been introduced to improve the resiliency of control systems against faults and failures. In general, FTC techniques are classified into active and passive approaches. This paper reviews fault and failure causes in control systems and discusses the latest solutions that are introduced to make the control system resilient.The recent achievements in fault detection and isolation (FDI) approaches and active FTC designs are investigated. Furthermore, a thorough comparison of several different aspects is conducted to understand the advantage and disadvantages of various FTC techniques to motivate researchers to further developing FTC and FDI approaches. Full article
(This article belongs to the Special Issue Machine Fault Detection and Fault-Tolerant Control)
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