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Imbalanced data fault diagnosis of rolling bearings using enhanced relative generative adversarial network

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Abstract

Rolling bearings, as integral components of rotating machinery, play a crucial role in ensuring the safe and stable operation of equipment. However, due to the inherent imbalance in the collected actual data, the accuracy of fault diagnosis is significantly compromised. An enhanced relative generative adversarial network (ERGAN) data augmentation model is proposed to improve fault diagnosis capability in imbalanced datasets. This paper introduces a relative loss function with a gradient penalty to enhance training stability and effectively address the mode collapse issue. Additionally, the generator and discriminator are reconstructed using one-dimensional convolutional layers and spectral normalization layers, which improves the quality of generated samples and effectively avoid gradient issues. Finally, an improved deep convolutional diagnostic model (DCNN) is established using variable convolutional kernels for fault classification. Experimental results demonstrate that, compared to other methods, this approach exhibits superior performance in imbalanced fault diagnosis tasks.

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Acknowledgments

The studies were funded by the National Natural Science Foundation of China (Grant numbers 61873142).

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China

    Jie Luo

  2. College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, 100101, China

    Yinong Zhang & Xin Jing

  3. Department of Automation, Tsinghua University, Beijing, 100084, China

    Fan Yang

Authors
  1. Jie Luo
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  2. Yinong Zhang
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  3. Fan Yang
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  4. Xin Jing
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Corresponding author

Correspondence to Yinong Zhang.

Additional information

Jie Luo received the B.Sc. in Software Engineering from Sichuan University Jinjiang College, Sichuan, China, in 2021. Currently, he is studying for the M.E at Beijing Union University, Beijing, China. His research interests include deep learning and fault diagnosis.

Yinong Zhang received the M.E in Control Science and Engineering from Tsinghua University. Currently, she is the Vice President of the School of Urban Rail Transit and Logistics of Beijing Union University. Her main research fields are complex process modeling, intelligent control, fault diagnosis and safety evaluation.

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Luo, J., Zhang, Y., Yang, F. et al. Imbalanced data fault diagnosis of rolling bearings using enhanced relative generative adversarial network. J Mech Sci Technol 38, 541–555 (2024). https://doi.org/10.1007/s12206-024-0104-2

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  • DOI: https://doi.org/10.1007/s12206-024-0104-2

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