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Intelligent Fault Diagnosis Method of Rolling Bearings Based on Transfer Residual Swin Transformer with Shifted Windows

Haomiao Wang1, Jinxi Wang2, Qingmei Sui2,*, Faye Zhang2, Yibin Li1, Mingshun Jiang2, Phanasindh Paitekul3

1 The Institute of Marine Science and Technology, Shandong University, Qingdao, 26623, China
2 The School of Control Sciences and Engineering, Shandong University, Jinan, 250061, China
3 Thailand Institute of Scientific and Technological Research, Amphoe Khlong Luang, Pathum Thani, 12120, Thailand

* Corresponding Author: Qingmei Sui. Email: email

Structural Durability & Health Monitoring 2024, 18(2), 91-110. https://doi.org/10.32604/sdhm.2023.041522

Abstract

Due to their robust learning and expression ability for complex features, the deep learning (DL) model plays a vital role in bearing fault diagnosis. However, since there are fewer labeled samples in fault diagnosis, the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields, which limits the diagnostic performance. To solve this problem, a novel transfer residual Swin Transformer (RST) is proposed for rolling bearings in this paper. RST has 24 residual self-attention layers, which use the hierarchical design and the shifted window-based residual self-attention. Combined with transfer learning techniques, the transfer RST model uses pre-trained parameters from ImageNet. A new end-to-end method for fault diagnosis based on deep transfer RST is proposed. Firstly, wavelet transform transforms the vibration signal into a wavelet time-frequency diagram. The signal’s time-frequency domain representation can be represented simultaneously. Secondly, the wavelet time-frequency diagram is the input of the RST model to obtain the fault type. Finally, our method is verified on public and self-built datasets. Experimental results show the superior performance of our method by comparing it with a shallow neural network.

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APA Style
Wang, H., Wang, J., Sui, Q., Zhang, F., Li, Y. et al. (2024). Intelligent fault diagnosis method of rolling bearings based on transfer residual swin transformer with shifted windows. Structural Durability & Health Monitoring, 18(2), 91-110. https://doi.org/10.32604/sdhm.2023.041522
Vancouver Style
Wang H, Wang J, Sui Q, Zhang F, Li Y, Jiang M, et al. Intelligent fault diagnosis method of rolling bearings based on transfer residual swin transformer with shifted windows. Structural Durability Health Monit . 2024;18(2):91-110 https://doi.org/10.32604/sdhm.2023.041522
IEEE Style
H. Wang et al., “Intelligent Fault Diagnosis Method of Rolling Bearings Based on Transfer Residual Swin Transformer with Shifted Windows,” Structural Durability Health Monit. , vol. 18, no. 2, pp. 91-110, 2024. https://doi.org/10.32604/sdhm.2023.041522



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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