A hybrid diagnosis method for inverter open-circuit faults in PMSM drives
Open accessAbstract
In order to improve the evaluation process of inverter open-circuit faults diagnosis in permanent magnet synchronous motor (PMSM) drives, this paper presents a diagnosis method based on current residuals and machine learning models. The machine learning models are introduced to make a comprehensive evaluation for the current residuals obtained from a state observer, instead of evaluating the residuals by comparing with thresholds. Meanwhile, fault diagnosis and location are conducted simultaneously by the machine learning models, which simplifies the diagnosis process. Besides, a sampling strategy is designed to implement the proposed scheme online. Experiments are carried out on a DSP based PMSM drive, and the effectiveness of the proposed method is verified.
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Transistors' abnormalities result mainly from aging changes, whose speed may increase significantly with increased intensity of drive operation (frequent overloads).
“Transistors' abnormalities result mainly from aging changes, the speed of which may increase significantly with the increase of the intensity of the drive operation, i.e. its frequent overloads [19], [243], [244].”
From Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges (2022) · cites this paper as [244]
The Introduction (page unavailable) discusses inverter faults and their effects, but does not identify transistor aging as their main cause. Searches for aging, thermal stress, and overloads found no passage linking frequent overloads to faster aging or more abnormalities. (LLM, conf=0.77)
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Cites
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Research works printed but not linked: 2
- Chen Yong ， Liu Zhilong， Chen Zhangyong. "Fast Diagnosis and Location Method for Open-Circuit Fault in Inverter Based on Current Vector Character Analysis," Transactions of China Electrotechnical Society，2018,33(04):883-891.
- Guangzhao Luo (Senior Member, IEEE) received the M.S. and Ph.D. degrees in electrical engineering from the Northwestern Polytechnical University (NPU), Xi'an, China, in 1998 and 2003, respectively. From 2003 to 2004, he was a Postdoctoral Research at the University of Federal Defense, Munich, Germany. He is currently a Professor with
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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 297 · see citing works
- Authors
- Zhang, Z., Luo, G., Zhang, Z., & Tao, X.
- Affiliations
- Northwestern Polytechnical University (CN)
- Venue
- CES Transactions on Electrical Machines and Systems
- Published
- 2020-09
- Volume
- 4
- Issue
- 3
- Pages
- 180-189
- ISSN
- 2096-3564 (ISSN-L)
- OpenAlex
- W3090145141
- Cited by
- 39
- References
- 21
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 4 · 2023: 14 · 2022: 6 · 2021: 2
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- CES Transactions on Electrical Machines and Systems (publishedVersion, cc-by-nc-nd)
- Lanzhou University Institutional Repository (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Sensorless Control of Electric Motors
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- permanent magnet synchronous motor, fault diagnosis, fault location, state observer, machine learning models, hybrid diagnosis, motor drives
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