Fault Feature Research of Rolling Bearing based on Empirical Mode Decomposition and Principle Component Analysis

Zheng Xin

doi:10.16578/j.issn.1004.2539.2016.01.012

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Fault Feature Research of Rolling Bearing based on Empirical Mode Decomposition and Principle Component Analysis

更新时间：2022-10-20

- Fault Feature Research of Rolling Bearing based on Empirical Mode Decomposition and Principle Component Analysis
- Vol. 40, Issue 1, Pages: 54-58(2016)
- 作者机构：
  
  1. 中山火炬职业技术学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2016.01.012
  CLC：
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[1]郑新.基于EMD与PCA分析的滚动轴承故障特征研究[J].机械传动,2016,40(01):54-58+63.

Zheng Xin. Fault Feature Research of Rolling Bearing based on Empirical Mode Decomposition and Principle Component Analysis. [J]. 40(1):54-58(2016)
[1]郑新.基于EMD与PCA分析的滚动轴承故障特征研究[J].机械传动,2016,40(01):54-58+63. DOI： 10.16578/j.issn.1004.2539.2016.01.012.

Zheng Xin. Fault Feature Research of Rolling Bearing based on Empirical Mode Decomposition and Principle Component Analysis. [J]. 40(1):54-58(2016) DOI： 10.16578/j.issn.1004.2539.2016.01.012.

摘要

研究提出了一种基于经验模式分解和多元统计过程控制的滚动轴承故障诊断方法,实现了对滚动轴承常见故障的分类研究。主要内容包括:首先,运用EMD将滚动轴承故障信号分解成不同频率尺度的单分量固有模态函数,获取了不同频率范围的信号成分;其次,对各个IMF分量进行主成分分析并建立稳态数据的主元模型,计算相应的T,2,控制限;最后,将待检测轴承信号的各个IMF分量代入主元模型,获取相应的T2值从而实现故障的判断。在故障检测的基础上,详细分析了各类滚动轴承故障与不同IMF分量间的相关性,确定了故障敏感主元向量,实现了故障种类的判断。研究为滚动轴承故障特征提取和故障分类提供了理论基础和有效方法。

Abstract

It is proposed that a fault diagnosis method for rolling bearing based on empirical mode decomposition（ EMD） and multivariate statistical process control（ MSPC）,the Hilbert- Huang transformation and principal component analysis（ PCA） are combined effectively in this method. It makes an effective classification for common bearing failure. The main contents are as follows: Firstly,the bearing signal is decomposed into intrinsic mode function under different frequency scale with EMD. Then,the PCA model of normal bearing with the IMFs is established and the control limits of Hotelling T2 of principal component model is calculated.Finally,take IMFs of signal to be detected into principal component model and the Hotelling T,2, value is obtained. The fault detection with T2 value is realized and principal component model. On the base of correlation between IMF functions and various faults,the classification is realized. This study gives an effective method and theoretical support for fault diagnosis and classification.

关键词

滚动轴承故障诊断经验模态分解主成分分析

Keywords

Rolling bearingFault diagnosisEmpirical mode decompositionPrinciple component analysis

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