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

Author

Cheng, Jia-tang ; Ai, Li ; Xiong, Wei

Author_Institution

Eng. Coll., Honghe Univ., Mengzi, China

Volume

1

fYear

2012

fDate

23-25 March 2012

Firstpage

24

Lastpage

26

Abstract

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.

Keywords

backpropagation; fault diagnosis; mechanical engineering computing; neural nets; particle swarm optimisation; rolling bearings; BP neural network; PSO; fault feature vectors; neighborhood operator; neural network optimization; particle swarm optimization hybrid algorithm; rolling bearing fault diagnosis; Accuracy; Fault diagnosis; Particle swarm optimization; Rolling bearings; Training; Vectors; Vibrations; BP Neural Network; fault diagnosis; particle swarm optimization with neighborhood operator; rolling bearing;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4673-0689-8

Type

conf

DOI

10.1109/ICCSEE.2012.369

Filename

6187820