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

被引:23
|
作者
Xu, Xiaowei [1 ]
Qiao, Xue [1 ]
Zhang, Nan [2 ]
Feng, Jingyi [1 ]
Wang, Xiaoqing [3 ]
机构
[1] Wuhan Univ Sci & Technol, Wuhan, Peoples R China
[2] Univ Nottingham, Nottingham, England
[3] Univ Alabama, Tuscaloosa, AL USA
基金
中国国家自然科学基金;
关键词
Electric vehicles; permanent magnet synchronous motor; intelligent fault diagnosis; deep learning; health management; ARTIFICIAL-INTELLIGENCE; NEURAL-NETWORK; EXPERT-SYSTEM; TEMPERATURE-FIELD; CIRCUIT FAULT; AUGMENTATION; METHODOLOGY; MACHINERY; RISK;
D O I
10.1177/1687814020944323
中图分类号
O414.1 [热力学];
学科分类号
摘要
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 multivariable working conditions and multi-physics environment.
引用
收藏
页数:14
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