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

doi:10.16182/j.issn1004731x.joss.201806038

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.

Key words: blind source separation, particle swarm optimization, genetic hybrids, bearing fault signal

CLC Number:

TN911.7

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.

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