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

Jiefei GU; Yang QI; Ziyi ZHAO; Wensheng SU; Lei SU; Ke LI; Michael PECHT

doi:10.51393/j.jamst.2022009

Volume 2 Issue 2

Mar. 2022

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Jiefei GU, Yang QI, Ziyi ZHAO, Wensheng SU, Lei SU, Ke LI, Michael PECHT. Fault diagnosis of rolling bearings based on generative adversarial network and convolutional denoising auto-encoder[J]. Journal of Advanced Manufacturing Science and Technology , 2022, 2(2): 2022009. doi: 10.51393/j.jamst.2022009

Citation:

Jiefei GU, Yang QI, Ziyi ZHAO, Wensheng SU, Lei SU, Ke LI, Michael PECHT. Fault diagnosis of rolling bearings based on generative adversarial network and convolutional denoising auto-encoder[J]. Journal of Advanced Manufacturing Science and Technology , 2022, 2(2): 2022009. doi: 10.51393/j.jamst.2022009

Citation:

Jiefei GU, Yang QI, Ziyi ZHAO, Wensheng SU, Lei SU, Ke LI, Michael PECHT. Fault diagnosis of rolling bearings based on generative adversarial network and convolutional denoising auto-encoder[J]. Journal of Advanced Manufacturing Science and Technology , 2022, 2(2): 2022009. doi: 10.51393/j.jamst.2022009

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

doi: 10.51393/j.jamst.2022009

a Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China;
b Special Equipment Safety Supervision Inspection Institute of Jiangsu Province, Nanjing 210003, China;
c Center for Advanced Life Cycle Engineering, University of Maryland, College Park MD 20742, USA

Funds:

This study was co-supported by the fellowship of the China Postdoctoral Science Foundation (No. 2021T140279), the National Natural Science Foundation of China (Nos. 11902124 and 52175096), and the 111 Project (No. B18027). The authors thank the Center for Advanced Life Cycle Engineering (CALCE) and its over 150 funding companies and the Centre for Advances in Reliability and Safety (CAiRS) at Hong Kong for enabling research into advanced topics in reliability, safety, and sustainment.

Received Date: 2022-01-10
Accepted Date: 2022-03-14
Rev Recd Date: 2022-02-18

Available Online: 2022-03-17

Publish Date: 2022-03-17

Abstract

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.
- Fault diagnosis,
- Rolling bearings,
- Generative adversarial network,
- Convolutional denoising auto-encoder,
- Small sample

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References(31)

References

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