Journal
APPLIED SOFT COMPUTING
Volume 92, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2020.106333
Keywords
Planetary gearbox fault diagnosis; Imbalanced sample dataset; Conditional variational generative; adversarial network; Sample generation; Adversarial learning
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Funding
- National Natural Science Foundation of China [61371041]
- Aeronautical Science Foundation of China [2013ZD52055]
- Jiangsu Province Graduate Research and Practice Innovation Program 2019, China [KYCX19_0171]
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In many real applications of planetary gearbox fault diagnosis, the number of fault samples is much less than normal samples while fault samples are hard to collected in different working conditions, so many traditional diagnosis methods will get low accuracy. To solve this problem, a method based on conditional variational auto-encoder generative adversarial network (CVAE-GAN) is proposed for imbalanced fault diagnosis. Firstly, new method uses encoder network of conditional variational auto-encoder to obtain the distribution of fault samples, and then a large number of similar fault samples can be generated through decoder network. Secondly, the parameters of generator, discriminator and classifier may be continuously optimized using adversarial learning mechanism. Finally, the trained CVAE-GAN is applied for intelligent fault diagnosis of planetary gearbox. The experimental results show that CVAE-GAN can generate fault samples in different working conditions, which improve the fault diagnosis performance of planetary gearbox. The sample generating ability of CVAE-GAN is significantly higher than other methods in two cases of imbalanced dataset. (C) 2020 Published by Elsevier B.V.
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