Rolling bearing fault diagnosis based on correlation frequency loss Wasserstein generative adversarial network

CHEN Jingdong; CHEN Xingjie; LYU Zhaomin

doi:10.12299/jsues.24-0205

Volume 40 Issue 1

Mar. 2026

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CHEN Jingdong, CHEN Xingjie, LYU Zhaomin. Rolling bearing fault diagnosis based on correlation frequency loss Wasserstein generative adversarial network[J]. Journal of Shanghai University of Engineering Science, 2026, 40(1): 23-29. doi: 10.12299/jsues.24-0205

Citation:

CHEN Jingdong, CHEN Xingjie, LYU Zhaomin. Rolling bearing fault diagnosis based on correlation frequency loss Wasserstein generative adversarial network[J]. Journal of Shanghai University of Engineering Science, 2026, 40(1): 23-29. doi: 10.12299/jsues.24-0205

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Rolling bearing fault diagnosis based on correlation frequency loss Wasserstein generative adversarial network

doi: 10.12299/jsues.24-0205

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

Received Date: 2024-07-18
Available Online: 2026-05-27
Publish Date: 2026-03-01

Abstract

Abstract

To address the decline in rolling bearing fault diagnosis performance caused by imbalanced fault samples, a fault diagnosis method based on correlation frequency loss generative adversarial network (GAN) was proposed. The mutual correlation between real and generated data was calculated to quantitatively analyze their differences in the frequency domain. which was used to guide model training, improve the similarity between the generated and real data, and overcome the difficulty of synthesizing fault frequencies in the data. Finally, a convolutional neural network (CNN) was employed for fault diagnosis. The proposed method is experimentally validated using the Case Western Reserve University bearing dataset. Under imbalance ratios of 10:1, 20:1 and 50:1, the sample generation capability of the correlation frequency loss GAN was significantly higher than that of other methods, leading to a marked improvement in the fault recognition rate. This results verifies the effectiveness and superiority of the proposed method.
- rolling bearing,
- imbalanced sample,
- generative adversarial network (GAN),
- frequency domain loss,
- fault diagnosis

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References(11)

References

[1]	MARTIN-DIAZ I, MORINIGO-SOTELO D, DUQUE-PEREZ O, et al. Early fault detection in induction motors using adaboost with im- balanced small data and optimized sampling[J] . IEEE Transactions on Industry Applications, 2020, 20(23): 14337 − 14346. doi: 10.1109/JSEN.2020.3008177
[2]	SAUFI S R, AHMAD Z A B, LEONG M S, et al. Gearbox fault diagnosis using a deep learning model with limited data sample[J] . IEEE Transactions on Industrial Informatics, 2020, 16(10): 6263 − 6271. doi: 10.1109/TII.2020.2967822
[3]	郭玉荣, 茅健, 赵嫚. 基于CNN与注意力BiLSTM的轴承剩余使用寿命预测方法[J] . 上海工程技术大学学报, 2023, 37(1): 96 − 104. doi: 10.3969/j.issn.1009-444X.2023.01.015
[4]	JIA F, LEI Y G, LU N, et al. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization[J] . Mechanical Systems and Signal Processing, 2018, 110: 349 − 367. doi: 10.1016/j.ymssp.2018.03.025
[5]	LEI Y G, YANG B, JIANG X W, et al. Applications of machine learning to machine fault diagnosis: A review and roadmap[J] . Mechanical Systems and Signal Processing, 2020, 138: 106587. doi: 10.1016/j.ymssp.2019.106587
[6]	ZHANG R F, LIU Y X. Research on development and application of support vector machine - Transformer fault diagnosis[C] //Proceedings of the International Symposium on Big Data and Artificial Intelligence. Hong Kong, China: Association for Computing Machinery, 2018: 262 − 268.
[7]	GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C] //Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal: MIT Press, 2014: 2672 − 2680.
[8]	RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[C] //Proceedings of the 4th International Conference on Learning Representations. San Juan: ICLR, 2016: 1 − 16.
[9]	ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein GAN[J] . arXiv:1701.07875, 2017.
[10]	QIN Z H, HUANG F G, PAN J F, et al. Improved generative adversarial network for bearing fault diagnosis with a small number of data and unbalanced data[J] . Symmetry, 2024, 16(3): 358. doi: 10.3390/sym16030358
[11]	BAI G L, SUN W, CAO C, et al. GAN-based bearing fault diagnosis method for short and imbalanced vibration signal[J] . IEEE Sensors Journal, 2024, 24(2): 1894 − 1904. doi: 10.1109/JSEN.2023.3337278