A Wavelet-Based Fault Diagnosis Approach for Permanent Magnet Synchronous Motors

2019 ◽  
Vol 34 (2) ◽  
pp. 761-772 ◽  
Author(s):  
Mehrdad Heydarzadeh ◽  
Mohsen Zafarani ◽  
Mehrdad Nourani ◽  
Bilal Akin
Keyword(s):  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Diagnosis Approach

scholarly journals Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles

2020 ◽  
Vol 12 (7) ◽  
pp. 168781402094432
Author(s):  
Xiaowei Xu ◽  
Xue Qiao ◽  
Nan Zhang ◽  
Jingyi Feng ◽  
Xiaoqing Wang
Keyword(s):  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Health Management ◽  
Development Trend ◽  
Operating Conditions ◽  
Intelligent Fault Diagnosis ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors

Permanent magnet synchronous motors are the main power output components of electric vehicles. Once a failure occurs, it will affect the vehicle’s power, stability, and safety. While as a complex field-circuit coupling system composed of machine-electric-magnetic-thermal, the permanent magnet synchronous motor of electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency, and communication characteristics make it difficult to diagnose faults. Based on the research of a list of related references, this article reviews the methods of intelligent fault diagnosis for electric vehicle permanent magnet synchronous motors. The research status and development trend of fault diagnosis are analyzed. It provides theoretical basis for motor fault diagnosis and health management in multi-variable working conditions and multi-physics environment.

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

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4467
Author(s):  
Lien-Kai Chang ◽  
Shun-Hong Wang ◽  
Mi-Ching Tsai
Keyword(s):  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Experimental Results ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Electrical Signals

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.

scholarly journals Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review

2019 ◽  
Vol 9 (10) ◽  
pp. 2116 ◽  
Author(s):  
Yong Chen ◽  
Siyuan Liang ◽  
Wanfu Li ◽  
Hong Liang ◽  
Chengdong Wang
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Data Driven ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Signal Processing Methods

Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields. In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the electrical, mechanical and magnetic faults of the permanent magnet synchronous motor are introduced. Next, common fault diagnosis methods, such as model-based fault diagnosis, different signal processing methods, and data-driven diagnostic algorithms are enumerated. The research summarized in this paper mainly includes fault performance, harmonic characteristics, different time-frequency analysis techniques, intelligent diagnosis algorithms proposed recently and so on.

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