A multi-scale feature fusion network-based fault diagnosis method for wind turbine bearings

Abstract

A fault diagnosis method based on a multi-scale feature fusion network (MSFF-CNN) is proposed for the problem that the vibration signals of wind turbine bearings are easily disturbed by noise, and feature extraction is harrowing. Compared with the traditional diagnosis method, which has two stages of manual feature extraction and fault classification, this method combines the two into one. First, based on the characteristics of the bearing vibration signal, the multi-scale kernel algorithm is used to learn features in parallel at different scales. Then, the features extracted at different scales are fused to obtain complementary and rich diagnostic information. Finally, the Softmax classifier is used to output the fault diagnosis results. The simulation is carried out through the bearing vibration data of Case Western Reserve University. The results show that the accuracy of bearing fault diagnosis reaches 99.17%, proving the proposed method’s high accuracy and effectiveness.