Unsupervised Feature-Preserving CycleGAN for Fault Diagnosis of Rolling Bearings Using Unbalanced Infrared Thermal Imaging Sample
The fault diagnosis of rolling bearing is of great significance in industrial safety. The method of infrared thermal image combined with neural network can diagnose the fault of rolling bearing in a non-contact manner, however its data in different scenes are often unbalanced and difficult to obtain...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers
2024
|
| Subjects: | |
| Online Access: | http://eprints.um.edu.my/45856/ https://doi.org/10.1109/ACCESS.2024.3365551 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The fault diagnosis of rolling bearing is of great significance in industrial safety. The method of infrared thermal image combined with neural network can diagnose the fault of rolling bearing in a non-contact manner, however its data in different scenes are often unbalanced and difficult to obtain. The generative adversarial networks can solve this problem by generating data with the required features. In this paper, an unsupervised learning framework named Feature-Preserving Cycle-Consistent Generative Adversarial Networks (FP-CycleGAN) is designed for defect detection in unbalanced rolling bearing infrared thermography sample. Since the classical Cycle-Consistent Generative Adversarial Networks (CycleGAN) often must balance the weights between generation, discrimination and consistency loss when doing the feature conversion from source domain to target domain, and the process often results in pattern collapse or feature loss. To avoid this problem, a new discriminator is designed to identify whether the generated image A and B belong to two different classes, and a new class loss are proposed. In order to better extract fault features and perform features migration, the new generator is reconstructed based on the U-Network structure, the convtraspose method of the up-sampling network is replaced by Bicubic Interpolation to effectively avoid the checkerboard effect of the generated images. The defect detection of the expanded dataset was performed using Residual Network and compared with the pre-expansion data to demonstrate the usability of the generated data and the superiority of the proposed FP-CycleGAN method for rolling bearing defect detection in small sample of infrared thermal images. |
|---|