Rolling bearings fault diagnosis based on two-stage signal fusion and deep multi-scale multi-sensor network

Abstract

In order to realize high-precision diagnosis of bearings faults in a multi-sensor detection environment, a fault diagnosis method based on two-stage signal fusion and deep multi-scale multi-sensor networks is proposed. Firstly, the signals are decomposed and fused using weighted empirical wavelet transform to enhance weak features and reduce noise. Secondly, an improved random weighting algorithm is proposed to perform a second weighted fusion of the signals to reduce the total mean square error. The fused signals are input into the deep multi-scale residual network, the feature information of different convolutional layers is extracted through dilated convolution, and the features are fused using pyramid theory. Finally, the bearings states are classified according to the fusion features. Experiment results show the effectiveness and superiority of this method.