基于粒子群优化的全变分降噪在轴承故障诊断中的应用

 钟先友,赵炎堃,胡君林

(三峡大学 机械与动力学院,湖北 宜昌 443002)
摘要:滚动轴承是旋转机械的重要部件。针对强噪声下滚动轴承振动信号故障特征难以提取的问题,提出了基于粒子群优化(PSO)全变分降噪(TVD)的滚动轴承故障诊断方法。通过粒子群优化算法确定全变分降噪的最优参数,然后利用优化的全变降噪算法对滚动轴承振动信号进行降噪,最后对降噪信号进行频谱分析获取滚动轴承故障特征。将所提方法应用于滚动轴承故障诊断,并与基于小波降噪的轴承诊断方法相比较,结果表明了本文方法的有效性和优越性。
关键词:滚动轴承;故障诊断;全变分降噪;粒子群优化
中图分类号:TH165+.3 文献标志码:A doi:10.3969/j.issn.1006-0316.2020.10.001
文章编号:1006-0316 (2020) 10-0001-06
Application of Total Variation Denoising Based on Particle Swarm Optimization in Bearing fault diagnosis
ZHONG Xianyou, ZHAO Yankun, HU Junlin
( College of Mechanical & Power Engineering, China Three Gorges University,Yichang 443002, China )
Abstract: Rolling bearings are important parts of rotating machinery. It is difficult to extract the fault characteristics of rolling bearing vibration signals under strong noise. In order to solve the problem, this paper proposes a rolling bearing fault diagnosis method, Total Variation Denoising (TVD) based on Particle Swarm Optimization (PSO). Firstly, a PSO algorithm is used to determine the optimal parameters of TVD. And then the optimized TVD algorithm is used to reduce the noise of the rolling bearing vibration signal. Finally the noise reduction signal is subjected to spectrum analysis to obtain the rolling bearing fault characteristics. The proposed method is applied to the fault diagnosis of rolling bearings. Compared with the bearing diagnosis method based on wavelet noise reduction, the results show the effectiveness and superiority of this method.
Key words:rolling bearing;fault diagnosis;total variation denoising;particle swarm optimization 
———————————————
收稿日期:2020-05-14
基金项目:国家自然基金项目(51975324);湖北省自然科学基金项目(2018CFB399);湖北省水电机械设计与维修重点实验室项目(2020KJX11、2017KJX08、2017KJX09);宜昌机器人与智能系统重点实验室项目(JXYC00005)
作者简介:钟先友(1977-),男,湖北武汉人,博士,副教授,主要研究方向为机械信号处理与故障诊断,E-mail:120244830@qq.com;赵炎堃(1996-),男,山西运城人,硕士研究生,主要研究方向为机械信号处理与故障诊断。
 

 

设为首页  |  加入收藏    |   免责条款
《机械》杂志版权所有     Copyright©2008-2012 Jixiezazhi.com All Rights Reserved 

  电话:028-85925070    传真:028-85925073    E-mail:jixie@vip.163.com

地址:四川省成都锦江工业开发区墨香路48号   邮编:610063

Powered by PageAdmin CMS