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Open Access

Fault diagnosis of rolling bearings based on generative adversarial network and convolutional denoising auto-encoder

Jiefei GUa,, Yang QIa, Ziyi ZHAOa, Wensheng SUb, Lei SUa( ), Ke LIa( ), Michael PECHTc
Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China
Special Equipment Safety Supervision Inspection Institute of Jiangsu Province, Nanjing 210003, China
Center for Advanced Life Cycle Engineering, University of Maryland, College Park MD 20742, USA

Peer review under responsibility of Editorial Committee of JAMST

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Abstract

The vibration signals of rolling bearings are susceptible to strong noise interference. In addition, the lacking of fault samples for rolling bearings increases the difficulty of fault diagnosis. A fault diagnosis model based on conditional generative adversarial network (CGAN) and convolutional denoising auto-encoder (CDAE) is proposed to solve these problems. CGAN is used to generate new samples with the same distribution as the real samples. In order to improve the anti-noise ability of the model, we use CDAE as the discriminator model of CGAN to extract more robust features and achieve more accurate discrimination and classification. The generator and the discriminator are optimized by the adversarial mechanism to improve the quality of sample generation and the accuracy of fault classification. The experimental results show that the CGAN-CDAE model has good anti-noise ability, and achieves good fault diagnosis performance of rolling bearings in the case of small samples and class imbalance.

Keywords

Fault diagnosisRolling bearingsGenerative adversarial networkConvolutional denoising auto-encoderSmall sample

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Journal of Advanced Manufacturing Science and Technology
Volume 2 Issue 2,
April 2022
DOI: 10.51393/j.jamst.2022009
Cite this article:
GU J, QI Y, ZHAO Z, et al. Fault diagnosis of rolling bearings based on generative adversarial network and convolutional denoising auto-encoder. Journal of Advanced Manufacturing Science and Technology, 2022, 2(2): 2022009. https://doi.org/10.51393/j.jamst.2022009

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Received: 10 January 2022
Revised: 18 February 2022
Accepted: 14 March 2022
Published: 15 April 2022
© 2022 JAMST All rights reserved.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0)，which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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