Rolling Bearing Fault Diagnosis based on DWT-BPNN

Abstract

For the fault diagnosis of rolling bearings, it is of great significance to improve the diagnostic accuracy. Therefore, this paper presents a rolling bearing fault diagnosis method which combines Daubechies wavelet (DW) with back propagation neural network (BPNN). Specifically, Daubechies wavelet transform is utilized to decompose the vibration signal of the original data in to different frequency components, which can be implemented to extract more prominent fault features. Then, the extracted features are input into BPNN classification model for fault diagnosis by training and testing. Finally, various experiments are carried out on the rolling bearing dataset of Western Reserve University to verify the effectiveness of this method. The results of this study demonstrate that the proposed method is able to reliably identify different fault categories with higher accuracy in comparison with the FT-BPNN methods based on Fourier transform under different loading conditions, and provides a new and effective method for the fault diagnosis of rolling bearings.