基于粒子群优化时变滤波经验模态分解的轴承故障诊断
岑立，钟先友
（三峡大学 机械与动力学院，湖北 宜昌 443002）
摘要：时变滤波经验模态分解（TVFEMD）的性能在很大程度上取决于其参数（即带宽阈值和B样条阶数）的选取。在应用TVFEMD诊断轴承故障时，参数需要预先人为设定，因此难以获得令人满意的分解结果。针对此情况，本文提出了一种基于粒子群优化时变滤波经验模态分解的轴承故障诊断方法。首先利用粒子群算法来搜索最佳参数组合；然后使用得到的最佳参数组合对轴承故障信号进行TVFEMD分解，得到一组本征模态函数（IMF）；最后选取包络谱故障特征能量比最大的IMF分量进行包络解调分析，提取故障特征，进行故障诊断。轴承故障诊断实例结果表明该方法不仅优化了TVFEMD两个参数，获得了良好分解效果，而且能够准确的提取轴承故障特征信息，实现轴承故障的有效诊断。
关键词：滚动轴承；粒子群；时变滤波经验模态分解；参数优化；故障诊断
中图分类号：TH133.3 文献标志码：A doi：10.3969/j.issn.1006-0316.2020.11.002
文章编号：1006－0316 (2020) 11－0008－09
Bearing Fault Diagnosis Based on Particle Swarm Optimized Time-Varying Filtering Empirical Mode Decomposition
CEN Li，ZHONG Xianyou
( College of Mechanical & Power Engineering, China Three Gorges University, Yichang 443002, China )
Abstract：The performance of time-varying filter empirical mode decomposition (TVFEMD) depends to a large extent on the selection of its parameters (i.e., bandwidth threshold and B-spline order). When applying TVFEMD to diagnose bearing faults, the parameters need to be set manually in advance. There is a lot of blindness and subjectivity, so it is difficult to obtain satisfactory decomposition results. In view of this situation, this paper proposes a bearing fault diagnosis method based on particle swarm optimized time-varying filter empirical mode decomposition. First, the particle swarm algorithm is used to search for the best parameter combination. Then, the obtained best parameter combination is used to perform the TVFEMD decomposition of the bearing fault signal to obtain a set of eigenmode functions (IMF). Finally, the one with the largest envelope spectrum fault feature energy ratio the IMF component is selected to perform the envelope demodulation analysis, extracts fault features and fault diagnosis. The results of the bearing fault diagnosis example show that the method not only realizes the automatic optimization of parameters and obtains a good decomposition effect, but also can accurately extract the bearing fault feature information and realize effective diagnosis of bearing faults.
Key words：rolling element bearings；particle swarm；time-varying filter empirical mode decomposition；parameter optimization；fault diagnosis
———————————————
收稿日期：2020－08－03
基金项目：国家自然基金（51975324）
作者简介：岑立（1997－），男，湖北汉川人，硕士研究生，主要研究方向为机械信号处理与故障诊断，E-mail：1561038034@qq.com；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。
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