Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification

Pei Junfeng; Sun Jianhua; Song Chuanzhi; Song Yupeng; Ge Huizhong

doi:10.16578/j.issn.1004.2539.2018.04.030

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Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification

更新时间：2022-10-20

- Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification
- Vol. 42, Issue 4, Pages: 150-155(2018)
- 作者机构：
  
  1. 常州大学机械工程学院
  2. 江苏省绿色过程装备重点实验室常州大学
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.04.030
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[1]裴峻峰,孙建华,宋传智,宋于鹏,葛慧中.基于EEMD消噪和相关系数识别的滚动轴承故障诊断方法[J].机械传动,2018,42(04):150-155.

Pei Junfeng, Sun Jianhua, Song Chuanzhi, et al. Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification. [J]. 42(4):150-155(2018)
[1]裴峻峰,孙建华,宋传智,宋于鹏,葛慧中.基于EEMD消噪和相关系数识别的滚动轴承故障诊断方法[J].机械传动,2018,42(04):150-155. DOI： 10.16578/j.issn.1004.2539.2018.04.030.

Pei Junfeng, Sun Jianhua, Song Chuanzhi, et al. Rolling Bearing Fault Diagnosis Method based on EEMD Denoising and Correlation Coefficient Identification. [J]. 42(4):150-155(2018) DOI： 10.16578/j.issn.1004.2539.2018.04.030.

摘要

针对往复泵动力端滚动轴承振动信号非平稳、非线性且含有大量噪声的特点,提出基于EEMD、距离因子、相关系数和小波包分解相结合的诊断方法。通过在轴承寿命试验台上测取轴承振动信号,利用EEMD对所测信号进行分解,根据距离因子和相关系数相结合的方法筛选并重构IMF分量,以突出故障特征信息且避免偶然误差影响,重构振动信号进行小波包分解后构造能量特征信号向量,然后计算特征向量的相关系数,通过比较所得相关系数的差值绝对值判断故障类型。此方法的故障识别率与直接对轴承振动信号进行相关系数分析相比,故障识别率有较大提高,而且无需像神经网络识别需要大量数据训练,是一种较好的轴承故障识别方法。

Abstract

In view of the nonlinear,non-stationary and massive noise features of reciprocating pump power of rolling bearing vibration signal,a fault diagnosis method of rolling bearing based on EEMD,distance factor,correlation coefficient and wavelet packet decomposition is proposed. By measuring the vibration signals of bearings in the bearing life test and decomposing the signals by using the method of EEMD,a number of IMF components is got. Then,the energy characteristic signal vectors is obtained through wavelet packet decomposing and constructing of reconstructed vibration signals,which got from the selecting and reconstructing of IMF components using the method of combination of distance factor and correlation coefficient. At last,the type of failure is determined based on the absolute value of the difference in the correlation coefficient of the energy characteristic signal vectors. Compared with the direct correlation coefficient analysis of the bearing vibration signal,this method has a high fault recognition rate,and doesn’t require a large amount of data training needed by neural networks,it is a better bearing fault identification method.

关键词

EEMD小波包分解距离因子相关系数滚动轴承故障诊断

Keywords

EEMDWavelet packet decompositionDistance factorCorrelation coefficientRolling bearingFault diagnosis

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