Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

• Autorzy: Zheng Wenfang , Wang Tieying
• Języki publikacji: EN

Abstrakty

EN

This study addresses the issue of diagnosing faults in electric vehicle motors and presents a method utilizing Improved Wavelet Packet Decomposition (IWPD) combined with particle swarm optimization (PSO). Initially, the analysis focuses on common demagnetization faults, inter turn short circuit faults, and eccentricity faults of permanent magnet synchronous motors. The proposed approach involves the application of IWPD for extracting signal feature vectors, incorporating the energy spectrum scale, and extracting the feature vectors of the signal using the energy spectrum scale. Subsequently, a binary particle swarm optimization algorithm is employed to formulate strategies for updating particle velocity and position. Further optimization of the binary particle swarm algorithm using chaos theory and the simulated annealing algorithm results in the development of a motor fault diagnosis model based on the enhanced particle swarm optimization algorithm. The results demonstrate that the chaotic simulated annealing algorithm achieves the highest accuracy and recall rates, at 0.96 and 0.92, respectively. The model exhibits the highest fault accuracy rates on both the test and training sets, exceeding 98.2%, with a minimal loss function of 0.0035. Following extraction of fault signal feature vectors, the optimal fitness reaches 97.4%. In summary, the model constructed in this study demonstrates effective application in detecting faults in electric vehicle motors, holding significant implications for the advancement of the electric vehicle industry.

Słowa kluczowe

EN

chaos theory; motor fault diagnosis; PSO; particle swarm optimization; simulated annealing algorithm; wavelet packet decomposition

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2024
• Tom: Vol. 73, nr 2
• Strony: 481--498
• Opis fizyczny: Bibliogr. 22 poz., fot., rys., tab., wykr., wz.

Twórcy

autor

Zheng Wenfang

• Xinxiang Vocational and Technical College, Xinxiang 453000, China

• https://orcid.org/0009-0006-3839-2849

autor

Wang Tieying

• Xinxiang Vocational and Technical College, Xinxiang 453000, China

• https://orcid.org/0009-0004-4287-5080

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