Spectral normalization CycleGAN for bearing fault transfer diagnosis

LI Jiesong1,2, LIU Tao1,2, WU Xing2,3

Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (24) : 282-289.

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PDF(2045 KB)
Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (24) : 282-289.

Spectral normalization CycleGAN for bearing fault transfer diagnosis

  • LI Jiesong1,2, LIU Tao1,2, WU Xing2,3
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Abstract

The advantage of deep learning without prior feature extraction makes it increasingly used in intelligent fault diagnosis of industrial equipment, but the low robustness and high data requirements of deep learning methods hinder practical applications. In order to adapt to the complex and variable working conditions in industrial sites, this paper proposes a SN_1DCycleGAN network based on spectral normalization (SN) and cycle-consistent adversarial networks (CycleGAN) for fault data transfer generation and diagnosis under variable working conditions. Firstly, a 1DCycleGAN network adapted to vibrational data generation is built for obtaining the mapping relationship between the source and target domains. The network is improved using spectral normalization to effectively prevent the training instability situation. Secondly, adaptive target data are obtained by changing the source domain to achieve the purpose of variable work transfer. Finally, the quality of data generation is quantitatively evaluated using three evaluation metrics as well as classifier accuracy, and validated using simulation and experimental signals. The experimental results show that SN_1DCycleGAN has a certain transfer effect on 1D vibration signals, which can enhance the variable working condition data and improve the accuracy of the classifier. The stability and generation quality are better than 1DCycleGAN.

Key words

intelligent fault diagnosis / cycle-consistent adversarial networks / spectral normalization / varia ble condition transfer generation

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LI Jiesong1,2, LIU Tao1,2, WU Xing2,3. Spectral normalization CycleGAN for bearing fault transfer diagnosis[J]. Journal of Vibration and Shock, 2023, 42(24): 282-289

References

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