基于GA-AW-PSO的动态盲源分离轴承故障检测研究

doi:10.16182/j.issn1004731x.joss.201806038

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (6): 2306-2314.doi: 10.16182/j.issn1004731x.joss.201806038

基于GA-AW-PSO的动态盲源分离轴承故障检测研究

张天骐, 马宝泽, 强幸子, 全盛荣

重庆邮电大学信号与信息处理重庆市重点实验室,重庆 400065

收稿日期:2016-08-09 修回日期:2016-12-27 出版日期:2018-06-08 发布日期:2018-06-14
作者简介:张天骐(1971-),男,四川眉山,博士,教授,研究方向为扩频信号盲处理、信号同步处理; 马宝泽(通讯作者1990-),男,河北廊坊,博士,研究方向为盲源分离算法及应用。
基金资助:
国家自然科学基金(61671095, 61371164, 61275099)

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

Zhang Tianqi, Ma Baoze, Qiang Xingzi, Quan Shengrong

Chongqing Key Laboratory of Signal and Information Processing (CQKLS & IP), Chongqing University of Posts and Telecommunications (CQUPT), Chongqing 400065, China

Received:2016-08-09 Revised:2016-12-27 Online:2018-06-08 Published:2018-06-14

摘要/Abstract

摘要： 针对动态混合轴承信号盲分离问题,提出一种基于遗传机制改进的自适应惯性权重粒子群(GA-AW-PSO)方法。该方法以分离信号负熵作为目标函数,依据粒子适应度差值自适应调节惯性权重,以减少无效迭代次数;同时引入遗传杂交机制,增加了种群的多样性,有利于处理动态混合信号;此外,为降低算法复杂度将正交矩阵表示为参数化的形式。仿真表明,该方法对动态混合的模拟机械信号盲分离时性能优于传统算法;能分离实际动态轴承信号,达到了故障检测目的。

关键词: 盲源分离, 粒子群, 遗传杂交, 轴承故障信号

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

中图分类号:

TN911.7

张天骐, 马宝泽, 强幸子, 全盛荣. 基于GA-AW-PSO的动态盲源分离轴承故障检测研究[J]. 系统仿真学报, 2018, 30(6): 2306-2314.

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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基于GA-AW-PSO的动态盲源分离轴承故障检测研究

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

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