Research on Fault Diagnosis of Rolling Bearing Based on SOM Neural Network

Abstract

In order to solve the problems of rolling bearing fault diagnosis and complex nonlinear pattern classification, according to the non-stationary and nonlinear characteristics of rolling bearing vibration signals, by analyzing the influence of bearing vibration signal training sample set, feature extraction methods and other factors on bearing fault diagnosis, a state clustering analysis method based on Self-Organizing Maps neural network is designed. In this method, five fault features of rolling bearings are extracted, and SOM neural network is used for training to obtain a bearing state discrimination model. Finally, the trained model is used to identify the fault types of rolling bearings. The verification of actual bearing fault data shows that the fault diagnosis method based on SOM neural network can accurately and effectively identify the fault of rolling bearing, and the accuracy rate is as high as 100%.