An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition

•A new diagnostic model named SDAE-GAN is proposed.•The model combines Generative Adversarial Networks and Stacked Denoising Autoencoders.•The performance of the model is investigated in the planetary gearbox experiment platform.•Results suggest that SDAE-GAN is better than SDAE and other common dia...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 310; S. 213 - 222
Hauptverfasser: Wang, Zirui, Wang, Jun, Wang, Youren
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 08.10.2018
Schlagworte:
Adversarial machine learning
Deep stacked denoising autoencoders
Fault diagnosis
Generative adversarial networks
Planetary gearbox
Small samples
Fault diagnosis
Generative adversarial networks
Deep stacked denoising autoencoders
Adversarial machine learning
Planetary gearbox
Small samples
ISSN:0925-2312, 1872-8286
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Zusammenfassung:•A new diagnostic model named SDAE-GAN is proposed.•The model combines Generative Adversarial Networks and Stacked Denoising Autoencoders.•The performance of the model is investigated in the planetary gearbox experiment platform.•Results suggest that SDAE-GAN is better than SDAE and other common diagnostic models in classification precision. Planetary gearbox has complex structures and works under various non-stationary operating conditions. The vibration signals of planetary gearbox are complicated and usually polluted by noise and interference. It is difficult to extract effective features of early faults. In addition, there are only a small number of fault samples for planetary gearbox fault diagnosis. All of these increase the difficulty of planetary gearbox fault diagnosis. Aiming at these problems, a novel fault diagnostic method is proposed which combines Generative Adversarial Networks (GAN) and Stacked Denoising Autoencoders (SDAE). The generator of GAN can generate new samples which has similar distribution with original samples from planetary gearbox vibration signals. Then, generated samples are transformed to the discriminator together with original samples which expand the sample size. SDAE is used as the discriminator of GAN which can automatically extract effective fault features from input samples and discriminate their authenticity and fault categories. Through novel adversarial machine learning mechanism, the generator and discriminator are concurrently optimized to enhance the quality of generation samples and the ability of fault mode classification. The experimental results show that the developed SDAE-GAN method for planetary gearbox has good anti-noise ability and achieve better fault diagnosis performance in the case of small samples.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2018.05.024