A New Fault Diagnosis Method for Rolling Bearings with the Basis of Swin Transformer and Generalized S Transform

Jin Yan, Xu Zhu, Xin Wang and Dapeng Zhang ()
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Jin Yan: Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang 524088, China
Xu Zhu: Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang 524088, China
Xin Wang: Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang 524088, China
Dapeng Zhang: Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang 524088, China

Mathematics, 2024, vol. 13, issue 1, 1-23

Abstract: In view of the rolling bearing fault signal non-stationarity, strong noise can lead to low fault diagnosis accuracy. A Swin Transformer and generalized S Transform fault diagnosis method is proposed to solve the problems of difficult signal feature extraction and low diagnostic accuracy. Generalized S transform is used to improve the resolution of bearing fault signals, the Swin Transformer model is used to master the shallow weight required for identifying rolling bearing faults for highly fault characteristic expression signals, and the deep weight is obtained by backpropagation training. Finally, the extracted features are input into the improved Softmax classifier for fault classification. The various signal processing methods for the bearing signal processing ability are compared, and this model’s diagnosis ability and the ability to resist noise are verified. The experimental results show that the method has a remarkable ability and an accuracy of above 90% in the anti-noise test and also has a good robustness.

Keywords: rolling bearing; vibration signal; fault diagnosis; Swin Transform; generalized S transform (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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