Gearbox fault diagnosis based on deep convolution generative adversarial network

Yiqiang Jiang; Chen He; Li Sun; Bin Wu

doi:10.1117/12.2611132

14 October 2021 Gearbox fault diagnosis based on deep convolution generative adversarial network

Yiqiang Jiang, Chen He, Li Sun, Bin Wu

Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119303S (2021) https://doi.org/10.1117/12.2611132
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

The existing gearbox fault diagnosis models often need a lot of fault data and corresponding tags to complete the model training, but the actual fault data is often less and unevenly distributed. Aiming at the situation that the gearbox fault data is scarce and unevenly distributed, this paper proposes a fault diagnosis method based on the Deep Convolution Generative Adversarial Network (DCGAN). First, the original vibration signals collected in the early stage are processed through data processing to form a sample data set and input into the DCGAN model for confrontation training, and virtual samples with real sample characteristics are generated to expand the sample data set. Then, the convolution neural network model is constructed to complete the fault diagnosis and get the results. The results show that the model trained by this method is more accurate when generating a large amount of data, and the diagnostic effect is significantly better.

Citation Download Citation

Yiqiang Jiang, Chen He, Li Sun, and Bin Wu "Gearbox fault diagnosis based on deep convolution generative adversarial network", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119303S (14 October 2021); https://doi.org/10.1117/12.2611132

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KEYWORDS

Data modeling

Convolution

Statistical modeling

3D modeling

Signal processing

Teeth

Convolutional neural networks

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