Classification and Recognition Method of Bearing Fault Based on SDP-CNN

Abstract

In view of the problems that the signal features are difficult to extract when a fault occurs in a rolling bearing, and the time domain image of the original vibration signal cannot obviously show the feature differences of different faults, and the direct deep feature learning and recognition will have a large impact on the system performance, etc., a bearing fault classification and recognition method based on symmetry dot pattern-convolutional neural network (SDP-CNN) is proposed. First, the SDP method is used to analyze the vibration signals of different faults, and the signal SDP images obtained can clearly show the feature differences of different faults; then, the SDP images are input into the CNN network for feature learning and state recognition; finally, Validation was performed using the Case Western Reserve University (CWRU) bearing dataset. The results show that the recognition accuracy of this method is 97.5%, which further verifies that the deep learning algorithm can adaptively extract the features of the SDP image and effectively identify bearing faults.