Fault diagnosis method for rolling bearing based on CAE-GAN

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (23) : 65-70.

LI Ke1,HE Jianguang1,SU Lei1,GU Jiefei1,BAO Linghao1,XUE Zhigang2

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Abstract

It is difficult to obtain rolling bearing fault samples so that training samples appear imbalanced distribution, which seriously affects the accuracy of the bearing intelligent fault diagnosis. Aiming at the problem of imbalanced distribution, a fault diagnosis method based on Constrained AutoEncoder-Generative Adversarial Network (CAE-GAN) is proposed, which is used to enhance the feature of fault samples to improve the accuracy of fault diagnosis. Firstly, CAE-GAN combines autoencoder and generative adversarial network to construct a coding-decoding-discriminating network so that the generator can better capture the distribution of real samples. To improve the quality of the generated samples, a method of distance constraint is proposed to constrain the distance between the different generated samples to avoid all the generated samples of the same type. Finally, the rolling bearing fault diagnosis experiments indicate that the proposed method can effectively improve the quality of the generated samples, solve the problem of imbalanced distribution and is better than other models.

Key words

rolling bearing / fault diagnosis / imbalanced samples / autoencoder / generative adversarial network / distance constraint

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LI Ke1,HE Jianguang1,SU Lei1,GU Jiefei1,BAO Linghao1,XUE Zhigang2. Fault diagnosis method for rolling bearing based on CAE-GAN[J]. Journal of Vibration and Shock, 2023, 42(23): 65-70

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