Intelligent diagnosis method based on GAN sample generation technology

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (18) : 153-160.

MA Bo1,2,3，CAI Weidong1，ZHAO Dali4

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Abstract

Intelligent fault diagnosis based data-driven is an important tool to monitor the health of equipment. However, in practical applications, it is difficult to obtain a sufficient effective fault data training intelligent diagnosis model. In view of the sufficient data of equipment health status and the underutilization of the existing intelligent diagnosis method, an intelligent diagnosis method based on GAN (Generation Adversarial Networks) sample generation technology is proposed. The health status data reflects the characteristics features of the device, and the knowledge of the fault mechanism reflects the common features of the device. Based on the fusion of the two features, the fault samples are obtained. By training the deep convolutional neural network, a diagnostic model is constructed for each device and realized fault diagnosis under different working conditions. The test results from the CWRU bearing data and the simulated fault data of the test bench show that the proposed method can achieve high diagnostic accuracy under variable load conditions and has better variation than the existing intelligent diagnostic methods, and have good working condition transfer ability.

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GAN / sample generation / fault mechanism / intelligent diagnosis / transfer learning

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MA Bo1,2,3，CAI Weidong1，ZHAO Dali4. Intelligent diagnosis method based on GAN sample generation technology[J]. Journal of Vibration and Shock, 2020, 39(18): 153-160

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