Dynamic Blind Source Separation Method of Bearing Fault Diagnosis Based on GA-AW-PSO

Authors

Tianqi Zhang, Chongqing Key Laboratory of Signal and Information Processing (CQKLS & IP), Chongqing University of Posts and Telecommunications (CQUPT), Chongqing 400065, China;
Baoze Ma, Chongqing Key Laboratory of Signal and Information Processing (CQKLS & IP), Chongqing University of Posts and Telecommunications (CQUPT), Chongqing 400065, China;
Xingzi Qiang, Chongqing Key Laboratory of Signal and Information Processing (CQKLS & IP), Chongqing University of Posts and Telecommunications (CQUPT), Chongqing 400065, China;
Shengrong Quan, Chongqing Key Laboratory of Signal and Information Processing (CQKLS & IP), Chongqing University of Posts and Telecommunications (CQUPT), Chongqing 400065, China;

Abstract

Abstract: The adaptive particle swarm optimization based on genetic mechanism (GA-AW-PSO) is proposed, aiming at blind source separation for dynamic hybrid bearing signals. The negentropy of separated signal is regarded as an objective function. The inertia weight is adjusted adaptively to reduce the invalid iterations according to the fitness difference. The introduction of genetic mechanism can increase diversity and is helpful for dynamic signal processing. The parameterized representation of orthogonal matrices can reduce the complexity of the algorithm. The simulation results show that the proposed method is superior to traditional blind source separation for the dynamic mechanical hybrid analog signal. It can effectively separate the actual dynamic bearing signal and reach the purposes of fault detection.

Recommended Citation

Zhang, Tianqi; Ma, Baoze; Qiang, Xingzi; and Quan, Shengrong (2018) "Dynamic Blind Source Separation Method of Bearing Fault Diagnosis Based on GA-AW-PSO," Journal of System Simulation: Vol. 30: Iss. 6, Article 38.
DOI: 10.16182/j.issn1004731x.joss.201806038
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss6/38

First Page

2306

Revised Date

2016-12-27

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201806038

Last Page

2314

CLC

TN911.7

Recommended Citation

Zhang Tianqi, Ma Baoze, Qiang Xingzi, Quan Shengrong. Dynamic Blind Source Separation Method of Bearing Fault Diagnosis Based on GA-AW-PSO[J]. Journal of System Simulation, 2018, 30(6): 2306-2314.

DOI

10.16182/j.issn1004731x.joss.201806038