The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A review of Permanent Magnet Synchronous Motor fault diagnosis
Abstract
This paper presents a review of Permanent Magnet Synchronous Motor (PMSM) fault diagnosis methods. Firstly, PMSM usual faults including electrical, mechanical, and magnetic faults are listed. In the third part, the various signal processing methods for PMSM are summarized. Finally, the artificial intelligence methods for PMSM fault diagnosis are reviewed, such as artificial neural network, fuzzy logic.
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Bearing damage leads to eccentricity, increased friction between the stator and rotor, and in extreme cases to inter-turn short circuits or insulation fire.
“Bearing damage leads to eccentricity, increased friction between the stator and rotor, and in extreme cases to ITSCs or insulation fire [15], [17], [24].”
From Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges (2022) · cites this paper as [24]
Citations
Cites
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A review on induction motor online fault diagnosis
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Broken Bearings and Eccentricity Fault Detection for a Permanent Magnet Synchronous Motor
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Neural network based fault detection of PMSM stator winding short under load fluctuation
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Back-EMF Based Detection of Stator Winding Inter-turn Fault for PM Synchronous Motor Drives
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Stator Short Circuits Detection in PMSM by means of Higher Order Spectral Analysis (HOSA)
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Inter-turn Short Circuit Fault Diagnosis for PMSM Based on Complex Gauss Wavelet
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Detection of faults in PMSM using Field Reconstruction Method and Mechanical Impulse Response
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Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges
Orlowska-Kowalska, T., Wolkiewicz, M., Pietrzak, P., Skowron, M., Ewert, P., Tarchala, G., Krzysztofiak, M., & Kowalski, C. T. (2022) · IEEE Access · doi:10.1109/access.2022.3180153 · Cited by 328
“Few conference papers were published as well [24], [25], presenting very preliminary review of condition monitoring and fault diagnosis techniques for PM motors.”
BackgroundMethodInfluentialFaults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
Chen, Y., Liang, S., Li, W., Liang, H., & Wang, C. (2019) · Applied Sciences · doi:10.3390/app9102116 · Cited by 194
Pietrzak, P., & Wolkiewicz, M. (2021) · Energies · doi:10.3390/en14061630 · Cited by 40
Metadata
- Authors
- ZhifuWang, Yang, J., Ye, H., & Zhou, W.
- Affiliations
- Beijing Institute of Technology (CN)
- ORCID
- Wei Zhou
- Venue
- 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)
- Publisher
- IEEE
- Published
- 2014-08
- Pages
- 1-5
- OpenAlex
- W2312204358
- Cited by
- 62
- References
- 29
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 9 · 2023: 7 · 2022: 7 · 2021: 6 · 2020: 5 · 2019: 5 · 2018: 3 · 2017: 5 · 2016: 3 · 2015: 1
- FWCI
- 4.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Motor Design and Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Sensorless Control of Electric Motors
- Keywords
- permanent magnet synchronous motor, fault diagnosis, electrical faults, mechanical faults, signal processing, artificial neural networks, fuzzy logic
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