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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
CWMS-GAN: A small-sample bearing fault diagnosis method based on continuous wavelet transform and multi-size kernel attention mechanism
PloS one, Vol.20(4), pp.1-19
11/04/2025
PMID: 40215467
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9 Record Views
Abstract
In industrial production, obtaining sufficient bearing fault signals is often extremely difficult, leading to a significant degradation in the performance of traditional deep learning-based fault diagnosis models. Many recent studies have shown that data augmentation using generative adversarial networks (GAN) can effectively alleviate this problem. However, the quality of generated samples is closely related to the performance of fault diagnosis models. For this reason, this paper proposes a new GAN-based small-sample bearing fault diagnosis method. Specifically, this study proposes a continuous wavelet convolution strategy (CWCL) instead of the traditional convolution operation in GAN, which can additionally capture the signal's frequency domain features. Meanwhile, this study designed a new multi-size kernel attention mechanism (MSKAM), which can extract the features of bearing vibration signals from different scales and adaptively select the features that are more important for the generation task to improve the accuracy and authenticity of the generated signals. In addition, the structural similarity index (SSIM) is adopted to quantitatively evaluate the quality of the generated signal by calculating the similarity between the generated signal and the real signal in both the time and frequency domains. Finally, we conducted extensive experiments on the CWRU and MFPT datasets and made a comprehensive comparison with existing small-sample bearing fault diagnosis methods, which verified the effectiveness of the proposed approach.
Details
- Title
- CWMS-GAN: A small-sample bearing fault diagnosis method based on continuous wavelet transform and multi-size kernel attention mechanism
- Creators
- Shun Yu - University of New South WalesZi Li - University of New South WalesJialin Gu - University of New South WalesRunpu Wang - University of New South WalesXiaoyu Liu - Peking UniversityLin Li - Southern Cross UniversityFusen Guo - University of New South WalesYuheng Ren - European Union University (Switzerland)
- Publication Details
- PloS one, Vol.20(4), pp.1-19
- Publisher
- PLOS One
- Identifiers
- 991013285342402368
- Copyright
- © 2025 Yu et al
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article