Demagnetization Fault Diagnosis Research of Permanent Magnet In-wheel Motor Based on Improved SDP-CNN Method

doi:10.3901/JME.260540

Abstract

Abstract: Data preprocessing relying on manual experience and the loss of information during feature extraction are two main challenges in the fault diagnosis of permanent-magnet in-wheel motors. A novel method is proposed for the diagnosis of permanent magnet demagnetization faults, which combines an improved symmetric dot pattern(SDP) with a convolutional neural network(CNN). First, based on the demagnetization characteristics of permanent magnet materials, an electromagnetic field prediction model is established to simulate motor operation under demagnetization fault conditions, and the back electromotive force signals are obtained from experiments. Next, the SDP method is employed to convert the back electromotive force signals into symmetrical snowflake images. To enhance the visualization of fault features, the parameters of SDP are adaptively optimized using an improved whale optimization algorithm(WOA), a classification database is established. Then, the database where feature extraction and classification are performed by using an improved CNN algorithm and a mapping relationship between fault information and image features is established for the diagnosis of demagnetization faults. The findings demonstrate that the fault diagnosis accuracy of the proposed approach has achieved 99.3%, marking a 1.7% enhancement compared to the conventional CNN method. Moreover, the diagnostic precision is maintained even under the influence of noise, ensuring its efficacy in diagnosing demagnetization faults across diverse operational environments.

Key words: permanent magnet in-wheel motor, demagnetization fault diagnosis, symmetrized dot pattern, whale optimization algorithm, convolutional neural network

CLC Number:

TG156

WANG Qiang, SHANG Zeming, WANG Zhiyong, REN Ze, YIN Guodong. Demagnetization Fault Diagnosis Research of Permanent Magnet In-wheel Motor Based on Improved SDP-CNN Method[J]. Journal of Mechanical Engineering, 2026, 62(14): 364-375.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260540

http://www.cjmenet.com.cn/EN/Y2026/V62/I14/364

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