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Processes
Special Issues
Fault Diagnosis and Condition Monitoring of Electrical Machines and Drives
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Fault Diagnosis and Condition Monitoring of Electrical Machines and Drives

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 10 July 2026 | Viewed by 1602

Special Issue Editors

Dr. Chenghao Sun

E-Mail Website
Guest Editor
School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210046, China
Interests: integrated starter generator; multi-phase synchronous generator; fault diagnosis; motor drive
Dr. Ningfei Jiao

E-Mail Website
Guest Editor
Department of Electrical Engineering, Northwestern Polytechnical University, Xi’an 710129, China
Interests: integrated starter-generator; PMSM; fault diagnosis; position-sensorless control; motor drive
Special Issues, Collections and Topics in MDPI journals
Dr. Ji Pang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Xi’an University of Posts & Telecommunications, Xi’an 710061, China
Interests: permanent magnet synchronous machines; brushless wound-rotor synchronous starter-generator; motor control; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of electric transportation, intelligent manufacturing, and energy technologies, the demand for high-reliability electrical machine systems in critical application scenarios has become increasingly prominent. Reducing maintenance costs and preventing unscheduled downtimes, which cause production and economic losses, are a priority for manufacturers and operators. The correct diagnosis and early detection of incipient faults lead to reduced maintenance costs and downtime for the process under consideration, and, in many cases, also prevent harmful and sometimes disruptive failures. Moreover, the comprehension of the fault occurrence conditions and the fault detection and location are the foundation for the study of effective fault-tolerant techniques, since the latter must always include fault detection, the isolation of failed component, and component reconfiguration.

This Special Issue aims to provide an opportunity for scientists, researchers, and practicing engineers to share and disseminate their latest discoveries and results in the fields, indicating the future trends for the fault diagnosis and condition monitoring of electrical machines and drives. Topics include, but are not limited to, the following research areas:

(1) Simulation techniques, algorithms, or other tools for modeling and simulation;

(2) Innovative sensors and their applications in motor systems;

(3) Intelligent sensing technologies and multi-source information fusion for electrical machine systems;

(4) Advanced fault diagnosis and condition monitoring methods.

Dr. Chenghao Sun
Dr. Ningfei Jiao
Dr. Ji Pang
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 250 words) can be sent to the Editorial Office for assessment.

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. Processes 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

  • electrical machines
  • fault diagnosis
  • condition monitoring
  • feature extraction
  • innovative sensors
  • interturn short circuit
  • faults of power devices
  • demagnetization
  • faults of bearings

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Published Papers (3 papers)

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Research

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20 pages, 6475 KB  
Article
Rolling Element Bearing Fault Diagnosis Based on Adversarial Autoencoder Network
by Wenbin Zhang, Xianyun Zhang and Han Xu
Processes 2026, 14(2), 245; https://doi.org/10.3390/pr14020245 - 10 Jan 2026
Viewed by 238
Abstract
Rolling bearing fault diagnosis is critical for the reliable operation of rotating machinery. However, many existing deep learning-based methods rely on complex signal preprocessing and lack interpretability. This paper proposes an adversarial autoencoder (AAE)-based framework that integrates adaptive, data-driven signal decomposition directly into [...] Read more.
Rolling bearing fault diagnosis is critical for the reliable operation of rotating machinery. However, many existing deep learning-based methods rely on complex signal preprocessing and lack interpretability. This paper proposes an adversarial autoencoder (AAE)-based framework that integrates adaptive, data-driven signal decomposition directly into a neural network. A convolutional autoencoder is employed to extract latent representations while preserving temporal resolution, enabling encoder channels to be interpreted as nonlinear signal components. A channel attention mechanism adaptively reweights these components, and a classifier acts as a discriminator to enhance class separability. The model is trained in an end-to-end manner by jointly optimizing reconstruction and classification objectives. Experiments on three benchmark datasets demonstrate that the proposed method achieves high diagnostic accuracy (99.64 ± 0.29%) without additional signal preprocessing and outperforms several representative deep learning-based methods. Moreover, the learned representations exhibit interpretable characteristics analogous to classical envelope demodulation, confirming the effectiveness and interpretability of the proposed approach. Full article
(This article belongs to the Special Issue Fault Diagnosis and Condition Monitoring of Electrical Machines and Drives)
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21 pages, 4304 KB  
Article
Multi-Condition Fault Diagnosis Method for Rolling Bearings Based on Enhanced Singular Spectrum Decomposition and Optimized MMPE + SVM
by Wenbin Zhang, Xianyun Zhang and Yingyin Chen
Processes 2025, 13(12), 4082; https://doi.org/10.3390/pr13124082 - 18 Dec 2025
Viewed by 321
Abstract
Aiming to improve the currently low accuracy of fault diagnosis due to the difficulty of extracting the non-stationary and nonlinear features of rolling bearing fault signals, a multi-condition fault diagnosis method for rolling bearings was proposed based on enhanced singular spectrum decomposition (ESSD), [...] Read more.
Aiming to improve the currently low accuracy of fault diagnosis due to the difficulty of extracting the non-stationary and nonlinear features of rolling bearing fault signals, a multi-condition fault diagnosis method for rolling bearings was proposed based on enhanced singular spectrum decomposition (ESSD), optimized multi-scale mean permutation entropy (MMPE), and support vector machine (SVM). Firstly, aiming to address the problem of singular spectrum decomposition (SSD) producing false components and signals with low energy proportions that cannot be accurately decomposed when the residual energy ratio is used as the final iteration termination condition, an enhanced singular spectral decomposition method is proposed. Secondly, the effect of the MMPE extraction of fault features depends on the selection of parameters, and after comprehensively considering the interaction between MMPE parameters, a method to optimize MMPE based on the particle swarm optimization (PSO) algorithm is proposed to maximize the performance of the extracted features. Finally, considering that the classification performance of SVM is affected by the penalty factor c and kernel function g, the fault characteristics proposed by ESSD + PSO - MMPE are identified by an SVM classifier model that is optimized by the particle swarm algorithm, so as to realize the effective diagnosis of multi-condition faults in rolling bearings. Using rolling bearing simulation signals, the Case Western Reserve University bearing dataset, and the online monitoring signal from the front bearings of a wind farm’s 1.5 MW wind turbine, the proposed method is compared with EMD + MMPE + SVM, SSD + MMPE + PSO - SVM, ESSD + MMPE + PSO - SVM, and other methods, and the results show that the proposed method can effectively identify multi-working faults in rolling bearings. Full article
(This article belongs to the Special Issue Fault Diagnosis and Condition Monitoring of Electrical Machines and Drives)
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Review

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34 pages, 3017 KB  
Review
Practical Application of Condition-Based Monitoring (CBM) Technologies in the Modern Manufacturing Industry: A Review
by Andres Hurtado Carreon and Stephen C. Veldhuis
Processes 2025, 13(12), 4084; https://doi.org/10.3390/pr13124084 - 18 Dec 2025
Viewed by 680
Abstract
The competitive nature of the modern manufacturing industry, coupled with the constant demand from consumers for high-quality products, push manufacturers to use their production machines beyond their capable operational limits. Condition monitoring and maintenance are crucial necessities to maintain the nominal operation of [...] Read more.
The competitive nature of the modern manufacturing industry, coupled with the constant demand from consumers for high-quality products, push manufacturers to use their production machines beyond their capable operational limits. Condition monitoring and maintenance are crucial necessities to maintain the nominal operation of these machines and ensure the quality of their production processes. The introduction of condition-based monitoring (CBM) from the Industry 4.0 movement opens various opportunities that ensure a machine’s nominal and reliable operation. However, a major gap still exists between newly researched CBM technologies and how to practically apply them in the modern industry, without increasing cost and diminishing their value. Therefore, this paper provides a comprehensive review of the recent research works in CBM that aim to fill this gap. Additionally, this review provides guidance for both researchers and industry practitioners focusing on implementing CBM. Finally, the review concludes with a discussion on the challenges that arise in CBM technologies, future trends, and recommendations. Full article
(This article belongs to the Special Issue Fault Diagnosis and Condition Monitoring of Electrical Machines and Drives)
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