ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA

CHEN ChongYang; XIONG BangShu; HUANG JianPing; MO Yan; LI XinMin

doi:10.16579/j.issn.1001.9669.2016.05.04

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ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA

更新时间：2022-09-22

- ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA
- Journal of Mechanical Strength Vol. 38, Issue 5, Pages: 922-926(2016)
- 作者机构：
  
  1. 南昌航空大学无损检测技术教育部重点实验室
  2. 中国直升机设计研究所直升机旋翼动力学国防科技重点实验室
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2016.05.04
  CLC：
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CHEN ChongYang, XIONG BangShu, HUANG JianPing, et al. ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA. [J]. 38(5):922-926(2016)
DOI：

CHEN ChongYang, XIONG BangShu, HUANG JianPing, et al. ROLLING BEARING FAULT DIAGNOSIS BASED ON LMD AND ICA. [J]. 38(5):922-926(2016) DOI： 10.16579/j.issn.1001.9669.2016.05.04.

摘要

针对局部均值分解(Local Mean Decomposition,LMD)在提取故障特征时易受到噪声干扰的问题,提出了一种基于局部均值分解和独立分量分析(Independent Component Analysis,ICA)的滚动轴承故障诊断方法。该方法首先采用LMD方法提取信号PF分量;其次,对PF分量进行ICA盲源分离,得到PF分量的估计信号,有效去除了分量中的噪声成分;然后,提取估计信号的互信息、相关系数和近似熵作为特征向量;最后,采用SVM对特征向量进行故障分类,通过特征提取和故障诊断实验,结果表明LMD-ICA方法的故障识别率明显高于传统LMD方法。

Abstract

For the problem of Local Mean Decomposition（ LMD） was easily affected by noise interference when in the extraction of fault features,a rolling bearing fault diagnosis method which based on LMD and Independent Component Analysis（ ICA） was proposed. Firstly,original signal was decomposed into a series of production functions（ PF） by the LMD method.Secondly,the estimate of PF was obtained after the PF components had been separated by ICA method,and the noise was effectively removed. Then,mutual information,correlation coefficient and approximate entropy which were extracted from the estimate of PF components were grouped together as a feature vector. Finally,the fault feature vectors were classified by SVM.The results of the feature extraction and fault diagnosis experiments show that the fault recognition rate of LMD-ICA method is significantly better than the traditional LMD method.

关键词

滚动轴承LMDICASVM

Keywords

Rolling bearingLMDICASVM

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