Rolling Bearing Fault Diagnosis Based on the Hybrid Algorithm of Particle Swarm Optimization with Neighborhood Operator

摘要：

In order to improve the accuracy of rolling bearing fault diagnosis, a hybrid algorithm of particle swarm optimization with neighborhood operator is applied. According to the fault feature vectors, PSO with neighborhood operator is applied to optimize the weight of BP neural network, then the fault diagnosis is accomplished via the optimized neural network. The simulation results show that this method has better classification results for rolling bearing fault diagnosis and has a certain practicality.

关键词： particle swarm optimization with neighborhood operator BP Neural Network rolling bearing fault diagnosis

作者: Jia-tang CHENG Li AI Wei XIONG

作者单位: The Engineering College of Honghe University, Mengzi, China

会议类型: 国际会议

会议名称: 2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

会议地点: 杭州

会议语种:英文

页码: 24-26

在线出版日期: 2012-03-23（万方平台首次上网日期，不代表论文的发表时间）