Rolling bearing fault diagnosis method based on adaptive spectral loss generative adversarial networks

HE Jiaxing; CHEN Xingjie; LYU Zhaomin

doi:10.12299/jsues.23-0254

Volume 38 Issue 4

Dec. 2024

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Article Navigation > Journal of Shanghai University of Engineering Science > 2024 > 38(4): 406-413

HE Jiaxing, CHEN Xingjie, LYU Zhaomin. Rolling bearing fault diagnosis method based on adaptive spectral loss generative adversarial networks[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 406-413. doi: 10.12299/jsues.23-0254

Citation:

HE Jiaxing, CHEN Xingjie, LYU Zhaomin. Rolling bearing fault diagnosis method based on adaptive spectral loss generative adversarial networks[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 406-413. doi: 10.12299/jsues.23-0254

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Rolling bearing fault diagnosis method based on adaptive spectral loss generative adversarial networks

doi: 10.12299/jsues.23-0254

School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2023-12-08
Publish Date: 2024-12-31

Abstract

Abstract

A rolling bearing fault diagnosis method based on adaptive spectrum loss generative adversarial networks was proposed. Firstly, the spectral distance was introduced to measure the frequency domain difference between generated data and real data. Secondly, adaptive spectrum loss was added to the generator loss to reduce the weight of simple frequency components, so as toadaptively focus on difficult-to-synthesize frequency components, to better guide the generative adversarial networks to generate fake samples more similar to real data. The proposed method was validated using the Case Western Reserve University bearing dataset. Compared with other methods, the adaptive spectrum loss generative adversarial networks can generate higher-quality samples and significantly improve fault recognition rate under sample imbalance conditions.
- rolling bearing,
- unbalanced samples,
- fault diagnosis,
- adaptive spectral loss,
- generative adversarial networks

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References(21)

References

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