Demagnetization Fault Diagnosis of a PMSM Using Auto-Encoder and K-Means Clustering

Lien-Kai Chang, Shun-Hong Wang and Mi-Ching Tsai
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Lien-Kai Chang: Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Shun-Hong Wang: Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Mi-Ching Tsai: Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan

Energies, 2020, vol. 13, issue 17, 1-12

Abstract: 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.

Keywords: fault diagnosis; unsupervised learning; permanent magnet synchronous motor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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