Rolling Bearing Fault Diagnosis Method Based on Stacked Denoising Autoencoder and Convolutional Neural Network

Abstract

The signal of rotating machine faults often exhibits strong nonlinearity and noise interference. Therefore. A fault diagnosis method towards non-stationary signal is proposed in this paper. A fault diagnosis model of combining stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed to solve the problem of difficult classification under strong noise environment. First, the SDAE model is utilized to reduce noise interference from the original data set. Then the processed data set is input into the CNN model for fault classification. The validity of the fault diagnosis model has been verified by the case western reserve university (CWRU) bearing data. The effectiveness of the method has been verified by comparison with other models.