Intelligent fault diagnosis of rolling bearings based on MDF and Swin Transformer

Zehua Li; Fang Liu; Ziyu Yuan; Xin Huang; Siwei Huang; Hongqing Chen; Yongbin Liu

doi:10.1117/12.3030611

5 June 2024 Intelligent fault diagnosis of rolling bearings based on MDF and Swin Transformer

Zehua Li, Fang Liu, Ziyu Yuan, Xin Huang, Siwei Huang, Hongqing Chen, Yongbin Liu

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131636Q (2024) https://doi.org/10.1117/12.3030611
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

A fault diagnosis model based on Motif Difference Field (MDF) and Swin Transformer is proposed to address the issue of scarce fault samples in actual working conditions, which leads to poor diagnostic and generalization capabilities of Deep Learning based fault diagnosis models. Using MDF instead of Gramian Angle Field (GAF), the one-dimensional signal is transformed into a two-dimensional image, retaining features while performing data augmentation; Using the Swin Transformer network model instead of the CNN network for bearing fault measurement. The results indicate that the model has higher accuracy and generalization compared to other deep learning methods.

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

Citation Download Citation

Zehua Li, Fang Liu, Ziyu Yuan, Xin Huang, Siwei Huang, Hongqing Chen, and Yongbin Liu "Intelligent fault diagnosis of rolling bearings based on MDF and Swin Transformer", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131636Q (5 June 2024); https://doi.org/10.1117/12.3030611

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