LAPSE:2023.26096
Published Article
LAPSE:2023.26096
Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering
March 31, 2023
In recent years, many motor fault diagnosis methods have been proposed by analyzing vibration, sound, electrical signals, etc. To detect motor fault without additional sensors, in this study, we developed a fault diagnosis methodology using the signals from a motor servo driver. Based on the servo driver signals, the demagnetization fault diagnosis of permanent magnet synchronous motors (PMSMs) was implemented using an autoencoder and K-means algorithm. In this study, the PMSM demagnetization fault diagnosis was performed in three states: normal, mild demagnetization fault, and severe demagnetization fault. The experimental results indicate that the proposed method can achieve 96% accuracy to reveal the demagnetization of PMSMs.
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Keywords
fault diagnosis, permanent magnet synchronous motor, unsupervised learning
Subject
Suggested Citation
Chang LK, Wang SH, Tsai MC. Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering. (2023). LAPSE:2023.26096
Author Affiliations
Chang LK: Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Wang SH: Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Tsai MC: Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan [ORCID]
Wang SH: Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Tsai MC: Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan [ORCID]
Journal Name
Energies
Volume
13
Issue
17
Article Number
E4467
Year
2020
Publication Date
2020-08-30
Published Version
ISSN
1996-1073
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Original Submission
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PII: en13174467, Publication Type: Journal Article
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LAPSE:2023.26096
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doi:10.3390/en13174467
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[v1] (Original Submission)
Mar 31, 2023
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Mar 31, 2023
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