A bearing fault diagnosis method based on Swin transformer optimization

Siyuan Cheng; Ying Bai

doi:10.1117/12.3040586

10 September 2024 A bearing fault diagnosis method based on Swin transformer optimization

Siyuan Cheng, Ying Bai

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325714 (2024) https://doi.org/10.1117/12.3040586
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

This article proposes an optimization method for bearing fault diagnosis based on Swin Transformer. Extract features from the bearing dataset through continuous wavelet transform; Then, the obtained features are input into the Swin Transformer model optimized by the HHO optimization algorithm for learning and classification. Output classification results, accuracy, and loss values. By comparing models such as CNN, LSTM, RESNET, GRU. The Swin Transformer model has an accuracy index of 21.01%, respectively; 8.19%; 5.63%; 2.71% increase. The Swin Transformer optimized by the HHO algorithm achieved an accuracy of 99.97%. Greatly improves the accuracy, robustness, and generalization of bearing fault diagnosis.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Siyuan Cheng and Ying Bai "A bearing fault diagnosis method based on Swin transformer optimization", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325714 (10 September 2024); https://doi.org/10.1117/12.3040586

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