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Machines
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Vibration Detection of Induction and PM Motors
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Vibration Detection of Induction and PM Motors

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

Deadline for manuscript submissions: closed (31 August 2025) | Viewed by 6812

Special Issue Editor

Dr. Jianfeng Hong

E-Mail Website
Guest Editor
School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: motor; vibration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The "Vibration Detection of Induction and PM Motors" Special Issue compiles cutting-edge research on fault detection, diagnosis, and control strategies for induction and PM motors. Its goal is to meet the growing demand for the efficient, reliable, and safe operation of power machinery amidst technological advancements and increasing complexity. It consists of peer-reviewed articles that explore various aspects of fault diagnosis based on vibration signals. These include innovative methodologies and techniques for the early detection and identification of faults in machinery components, adaptive and robust control approaches, advanced data analytics for improved fault diagnosis, real-time monitoring and health management systems, and practical applications in diverse power machinery domains. In addition, this Special Issue discusses the challenges faced by researchers and practitioners in implementing these advanced techniques, including scalability, computational complexity, and the need for reliable performance in uncertain environments. By showcasing the latest developments and addressing the challenges in fault diagnosis of electrical machinery, this Special Issue serves as a valuable resource for researchers, engineers, and policymakers in the field.

Dr. Jianfeng Hong
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 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. 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 motor
  • PM motor
  • vibration signal
  • fault detection

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Published Papers (3 papers)

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Research

30 pages, 4602 KB  
Article
Intelligent Fault Diagnosis of Ball Bearing Induction Motors for Predictive Maintenance Industrial Applications
by Vasileios I. Vlachou, Theoklitos S. Karakatsanis, Stavros D. Vologiannidis, Dimitrios E. Efstathiou, Elisavet L. Karapalidou, Efstathios N. Antoniou, Agisilaos E. Efraimidis, Vasiliki E. Balaska and Eftychios I. Vlachou
Machines 2025, 13(10), 902; https://doi.org/10.3390/machines13100902 - 2 Oct 2025
Cited by 5 | Viewed by 2976
Abstract
Induction motors (IMs) are crucial in many industrial applications, offering a cost-effective and reliable source of power transmission and generation. However, their continuous operation imposes considerable stress on electrical and mechanical parts, leading to progressive wear that can cause unexpected system shutdowns. Bearings, [...] Read more.
Induction motors (IMs) are crucial in many industrial applications, offering a cost-effective and reliable source of power transmission and generation. However, their continuous operation imposes considerable stress on electrical and mechanical parts, leading to progressive wear that can cause unexpected system shutdowns. Bearings, which enable shaft motion and reduce friction under varying loads, are the most failure-prone components, with bearing ball defects representing most severe mechanical failures. Early and accurate fault diagnosis is therefore essential to prevent damage and ensure operational continuity. Recent advances in the Internet of Things (IoT) and machine learning (ML) have enabled timely and effective predictive maintenance strategies. Among various diagnostic parameters, vibration analysis has proven particularly effective for detecting bearing faults. This study proposes a hybrid diagnostic framework for induction motor bearings, combining vibration signal analysis with Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) in an IoT-enabled Industry 4.0 architecture. Statistical and frequency-domain features were extracted, reduced using Principal Component Analysis (PCA), and classified with SVMs and ANNs, achieving over 95% accuracy. The novelty of this work lies in the hybrid integration of interpretable and non-linear ML models within an IoT-based edge–cloud framework. Its main contribution is a scalable and accurate real-time predictive maintenance solution, ensuring high diagnostic reliability and seamless integration in Industry 4.0 environments. Full article
(This article belongs to the Special Issue Vibration Detection of Induction and PM Motors)
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22 pages, 7905 KB  
Article
Detecting Particle Contamination in Bearings of Inverter-Fed Induction Motors: A Comparative Evaluation of Monitoring Signals
by Tomas Garcia-Calva, Óscar Duque-Perez, Rene J. Romero-Troncoso, Daniel Morinigo-Sotelo and Ignacio Martin-Diaz
Machines 2025, 13(4), 269; https://doi.org/10.3390/machines13040269 - 25 Mar 2025
Cited by 1 | Viewed by 1260
Abstract
In induction motor bearings, distributed faults are prevalent, often resulting from factors such as inadequate lubrication and particle contamination. Unlike localized faults, distributed faults produce complex and unpredictable motor signal behaviors. Although existing research predominantly addresses localized faults in mains-fed motors, particularly single-point [...] Read more.
In induction motor bearings, distributed faults are prevalent, often resulting from factors such as inadequate lubrication and particle contamination. Unlike localized faults, distributed faults produce complex and unpredictable motor signal behaviors. Although existing research predominantly addresses localized faults in mains-fed motors, particularly single-point defects, a comprehensive investigation into particle contamination in bearings of inverter-fed motors is essential for a more accurate understanding of real-world bearing issues. This paper conducts a comparative analysis of vibration, stator current, speed, and acoustic signals to detect particle contamination through signal analysis across three domains: time, frequency, and time-frequency. These domains are analyzed to assess and compare the characteristics of each monitored signal in the context of bearing wear detection. The data were collected from both steady-state and startup transients of an induction motor controlled by a variable frequency drive. The experimental results highlight the most significant characteristics of each monitored signal, evaluated across the different domains of analysis. The primary conclusion indicates that, in inverter-fed motors, sound and vibration signals exhibit abnormal levels when the bearing is damaged but the related-fault signature becomes complicated. Additionally, the findings demonstrate that the analysis of startup stator current and speed signals presents the potential to detect distributed bearing damage in inverter-fed induction motors. Full article
(This article belongs to the Special Issue Vibration Detection of Induction and PM Motors)
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16 pages, 11430 KB  
Article
Analysis of Electromagnetic Vibration in Permanent Magnet Motors Based on Random PWM Technology
by Chi Ma, Yongxiang Wang, Huang Chen, Jianfeng Hong and Yi Wang
Machines 2025, 13(4), 259; https://doi.org/10.3390/machines13040259 - 22 Mar 2025
Cited by 3 | Viewed by 1567
Abstract
High vibration noise limits the application of permanent magnet motors in electric locomotive traction. This paper focuses on the high-frequency electromagnetic vibration in traction permanent magnet motors introduced by inverters. It explores the impact of periodic and random switching frequency pulse-width modulation (PWM) [...] Read more.
High vibration noise limits the application of permanent magnet motors in electric locomotive traction. This paper focuses on the high-frequency electromagnetic vibration in traction permanent magnet motors introduced by inverters. It explores the impact of periodic and random switching frequency pulse-width modulation (PWM) schemes on the high-frequency electromagnetic vibration performance of permanent magnet motors. The studied works are as follows: (1) The sources of higher-order harmonic components in the stator current are analyzed, and the characteristics of electromagnetic forces generated by these higher-order harmonic currents are studied. (2) The principles for suppressing high-frequency electromagnetic vibrations through random PWM are introduced. (3) The impact of the random switching frequency on higher-order harmonic currents in permanent magnet motors is analyzed through simulations. (4) The comprehensive experimental validation and evaluation of the random PWM technique are conducted on a permanent magnet motor. The results show that the vibration near the carrier frequency can be effectively weakened, but the overall vibration level has not been effectively reduced. Full article
(This article belongs to the Special Issue Vibration Detection of Induction and PM Motors)
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