Intelligent diagnosis method of rolling bearing based on BiGAN

Abstract

Rolling bearing is a critical component in the rotating machinery, which directly affects the reliability of the equipment. The artificial intelligence-enabled bearing fault diagnosis model has achieved impressive successes over the years. However, rolling bearings’ imbalanced data sets (normal samples are much larger than failure samples) degrade the diagnostic performance. To address this issue, a bidirectional generative adversarial network(BiGAN) based fault diagnosis method was proposed. First, the signal was denoised via the ensemble empirical mode decomposition(EEMD) to automatically distribute it to a suitable reference scale and avoid modal aliasing. Then, the BiGAN model with gradient penalty term was constructed to expand the fault samples, where the min-max normalization was included. Finally, based on the enhanced training set, the convolutional neural network was established with batch normalization and maximum pooling layers. Experimental results proved that the proposed method improved fault diagnosis accuracy and robustness.

Keywords

rolling bearing / fault diagnosis / bidirectional generative adversarial network (BiGAN) / convolutional neural network (CNN) / data imbalance

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