Characteristic analysis of multi-source fault information of motor stator

Wang Yali; Xu Baichuan; Wang Yi; Luo Chaoyueling; Huang Yuhao; Yang Kai

doi:10.1117/12.2640196

1 August 2022 Characteristic analysis of multi-source fault information of motor stator

Wang Yali, Xu Baichuan, Wang Yi, Luo Chaoyueling, Huang Yuhao, Yang Kai

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 1225737 (2022) https://doi.org/10.1117/12.2640196
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

The damage of stator armature winding inter-turn short circuit fault to motor determines the necessity of reliable and effective diagnosis in the early stage of fault. Most of the existing fault diagnosis methods are based on single fault feature detection by sensors, which is difficult to ensure long-term diagnostic accuracy. Based on the multi-source information fusion theory, this paper conducts a multi-physics coupling simulation of the stator fault by constructing the finite element model of the motor under different working conditions and analyzes the change of the multi-physics characteristics of the motor stator under the inter-turn short-circuit fault. Using electromagnetic, thermal, mechanical, and other characteristic parameters to diagnose stator winding faults can effectively and accurately identify fault types, and greatly improve the reliability of synchronous electronic stator winding fault diagnosis.

Citation Download Citation

Wang Yali, Xu Baichuan, Wang Yi, Luo Chaoyueling, Huang Yuhao, and Yang Kai "Characteristic analysis of multi-source fault information of motor stator", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 1225737 (1 August 2022); https://doi.org/10.1117/12.2640196

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