An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnosti...

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Bibliographic Details
Published in:World electric vehicle journal 2024-04, Vol.15 (4), p.165
Main Authors: Jiang, Yutao, Ji, Baojian, Zhang, Jin, Yan, Jianhu, Li, Wenlong
Format: Article
Language:English
Subjects:
Artificial intelligence
artificial intelligence (AI)
Efficiency
Electric vehicles
Estimates
Fault detection
fault detection and diagnosis
Fault diagnosis
inter-turn short fault (ITSF)
Kalman filters
Machine learning
Methods
Neural networks
Parameter estimation
Permanent magnets
PMSM
Power
Signal processing
Signatures
State estimation
State-of-the-art reviews
Stators
Synchronous motors
Winding
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Summary:This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications.
ISSN:2032-6653
2032-6653
DOI:10.3390/wevj15040165