Rolling Bearing Fault Classification Based on Stacked Denoising Auto Encoders

Abstract

Aiming at the characteristics of large capacity and diversity of rolling bearing fault data, an intelligent rolling bearing fault diagnosis method based on stacked denoising auto encoders was proposed. Firstly, Principal Component Analysis was used to reduce the dimension of the original data, and the redundant information is deleted. Then, three de-noising auto-encoders are created to train the bearing data. Then, a stack de-noising auto-encoder with three hidden layers is stacked with the trained DAE for reverse optimization. Finally, the features are input into soft-max classifier to realize rolling bearing fault diagnosis. The experimental results show that SDAE network can extract fault features effectively, and it is better than back propagation neural network in generalization and fault classification accuracy.