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Energies
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Fault Diagnosis in Electric Motors Ⅱ
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Fault Diagnosis in Electric Motors Ⅱ

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 9288

Special Issue Editor

Prof. Dr. Alberto Bellini

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", Alma Mater Studiorum, University of Bologna, Bologna, Italy
Interests: electric drives; fault diagnosis of electric machines; energy conversion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electrical machines are critical components of many industrial processes. Currently, much attention is being paid to energy-efficient processes, and within this framework electrical machines have increased their application areas. However, this requires new technologies for drives, power converters, and machines. Hence, reliability has become a critical issue, since for performance and efficiency reasons, high-frequency power signals are fed into machines, intrinsically reducing the lifetime of insulation and magnetic materials. In this context, the fault detection and diagnosis of electrical machines are of increasing importance and mature technologies are required for “open-loop” system fails for electric drives.

This Special Issue will focus on emerging technologies for efficient, non-invasive and online diagnosis of electrical machines. Topics of interest for publication include, but are not limited to:

  • Fault diagnosis of electrical machines and drives.
  • Specialized signal processing techniques for fault analysis.
  • Fault tolerant electrical systems, including multi-phase machines and/or redundant systems.
  • Fault diagnosis of power supply for electrical machines.
  • Fault diagnosis of electrical power generators.
  • Digital technologies for fault tolerant systems.

Prof. Dr. Alberto Bellini
Guest Editor

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. Energies 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 2600 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

  • diagnosis
  • digital technologies
  • electric drives
  • electric machines

Published Papers (3 papers)

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Research

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20 pages, 7717 KiB  
Article
Analysis of the Impact of Stator Inter-Turn Short Circuits on PMSM Drive with Scalar and Vector Control
by Mateusz Krzysztofiak, Maciej Skowron and Teresa Orlowska-Kowalska
Energies 2021, 14(1), 153; https://doi.org/10.3390/en14010153 - 30 Dec 2020
Cited by 22 | Viewed by 2651
Abstract
Permanent Magnet Synchronous Motor (PMSM) failures are currently widely discussed in the literature, but the impact of these failures on the operation of control systems and the ability to detect selected failures despite the compensating effect of control algorithms being relatively rarely analyzed. [...] Read more.
Permanent Magnet Synchronous Motor (PMSM) failures are currently widely discussed in the literature, but the impact of these failures on the operation of control systems and the ability to detect selected failures despite the compensating effect of control algorithms being relatively rarely analyzed. The article presents the impact of damage to the stator winding of a PMSM motor on the operation of two frequency control structures, scalar and vector control. The mathematical model of PMSM that takes into account the influence of a different number of shorted turns in the stator winding phase was presented, and its experimental verification was performed. Then, the influence of various degrees of damage to the stator winding on the waveforms of the motor state variables in an open scalar control structure and in a closed field-oriented control structure was analyzed. Based on the analysis of phase currents and rotational speed of the motor as well as the influence of the PMSM motor operating conditions, the basic techniques of extracting the symptoms of stator winding inter-turn short-circuits were analyzed, and the failure indicators were developed, which enable simple diagnostics of the stator winding. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors Ⅱ)
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15 pages, 5507 KiB  
Article
Design of Low-Cost Synchronous Machine to Prevent Demagnetization
by Claudio Bianchini, Ambra Torreggiani, Matteo Davoli and Alberto Bellini
Energies 2020, 13(14), 3566; https://doi.org/10.3390/en13143566 - 10 Jul 2020
Cited by 2 | Viewed by 2056
Abstract
The request for high efficiency motor paves the way for the replacement of induction motors with permanent magnet synchronous motors. Although the efficiency is increased, for medium and high power, the current ripple causes significant additional losses in the magnet and lamination; and, [...] Read more.
The request for high efficiency motor paves the way for the replacement of induction motors with permanent magnet synchronous motors. Although the efficiency is increased, for medium and high power, the current ripple causes significant additional losses in the magnet and lamination; and, high temperature can lead to demagnetization. In this paper, a new rotor topology is proposed and compared to a traditional surface permanent magnet rotor to reduce the magnet losses and protect them from demagnetization. A reference surface permanent magnet machine is compared with the proposed one in terms of performance and magnet losses. Both analytical and experimental analysis are carried out and discussed. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors Ⅱ)
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Review

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26 pages, 5494 KiB  
Review
Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review
by Yuanyuan Yang, Md Muhie Menul Haque, Dongling Bai and Wei Tang
Energies 2021, 14(21), 7017; https://doi.org/10.3390/en14217017 - 26 Oct 2021
Cited by 24 | Viewed by 3865
Abstract
Electric motors are used extensively in numerous industries, and their failure can result not only in machine damage but also a slew of other issues, such as financial loss, injuries, etc. As a result, there is a significant scope to use robust fault [...] Read more.
Electric motors are used extensively in numerous industries, and their failure can result not only in machine damage but also a slew of other issues, such as financial loss, injuries, etc. As a result, there is a significant scope to use robust fault diagnosis technology. In recent years, interesting research results on fault diagnosis for electric motors have been documented. Deep learning in the fault detection of electric equipment has shown comparatively better results than traditional approaches because of its more powerful and sophisticated feature extraction capabilities. This paper covers four traditional types of deep learning models: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN), and highlights their use in detecting faults of electric motors. Finally, the issues and obstacles that deep learning encounters in the fault detection mechanism as well as the prospects are discussed and summarized. Full article
(This article belongs to the Special Issue Fault Diagnosis in Electric Motors Ⅱ)
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