doi:10.16186/j.cnki.1673-9787.2019060034

Received:2019/06/13

Revised:2019/11/08

Published:2021/01/15

Fault diagnosis of rolling bearing based on PSO-VMD and Bayesian network

TONG Zhaojing, LU Tong, QIN Zini

School of Electrical Engineering and Automation， Henan Polytechnic University ，Jiaozuo 454000 ， Henan， China

Abstract:In order to solve the problems of difficulty in feature extraction and low fault recognition rate under variable load running condition of motor， a rolling bearing fault diagnosis model based on combination of variable mode decomposition function optimized by variable step size particle swarm and Bayesian networks was put forward. The fault information was extracted and discretized by the variational mode decomposition optimized by the variable step size particle swarm optimization algorithm and Hilbert transform. And then， a Bayesian networks diagnosis model with fault types was constructed， which inferred the probability of rolling bearing fault occurrence. The complete data set， the incomplete data set， and the noise experiments were used to verify the accuracy of the method. The simulation results showed that the proposed method could effectively extract feature information and predict estimation of uncertain information. It improved the accuracy of rolling bearing fault diagnosis， and was very promising in the fault diagnosis and prediction of rolling bearings.

Key words:variational mode decomposition;particle swarm optimization;Bayesian network;rolling bearing;fault diagnosis

 基于PSO_VMD与贝叶斯网络的滚动轴承故障诊断_仝兆景.pdf