Application of Optimal Noise Parameter Ensemble Local Mean Decomposition and Spectral Kurtosis in Bearing Fault Diagnosis

Wang Jianguo; Chen Shuai; Zhang Chao

doi:10.16578/j.issn.1004.2539.2017.05.034

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Application of Optimal Noise Parameter Ensemble Local Mean Decomposition and Spectral Kurtosis in Bearing Fault Diagnosis

更新时间：2022-10-20

- Application of Optimal Noise Parameter Ensemble Local Mean Decomposition and Spectral Kurtosis in Bearing Fault Diagnosis
- Vol. 41, Issue 5, Pages: 170-175(2017)
- 作者机构：
  
  1. 内蒙古科技大学机械工程学院
- 作者简介：
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- DOI：10.16578/j.issn.1004.2539.2017.05.034
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[1]王建国,陈帅,张超.噪声参数最优ELMD与谱峭度在滚动轴承故障诊断中的应用[J].机械传动,2017,41(05):170-175.

Wang Jianguo, Chen Shuai, Zhang Chao. Application of Optimal Noise Parameter Ensemble Local Mean Decomposition and Spectral Kurtosis in Bearing Fault Diagnosis. [J]. 41(5):170-175(2017)
[1]王建国,陈帅,张超.噪声参数最优ELMD与谱峭度在滚动轴承故障诊断中的应用[J].机械传动,2017,41(05):170-175. DOI： 10.16578/j.issn.1004.2539.2017.05.034.

Wang Jianguo, Chen Shuai, Zhang Chao. Application of Optimal Noise Parameter Ensemble Local Mean Decomposition and Spectral Kurtosis in Bearing Fault Diagnosis. [J]. 41(5):170-175(2017) DOI： 10.16578/j.issn.1004.2539.2017.05.034.

摘要

为了精准、稳定地提取滚动轴承故障特征,提出一种噪声参数最优总体局部均值分解（Ensemble Local Mean Decomposition,ELMD）与谱峭度（Spectral Kurtosis,SK）相结合的轴承故障诊断新方法。首先引入相对均方根误差确定ELMD方法中的最优噪声幅值;然后对故障信号进行噪声参数最优ELMD分解,并选取具有最大相关性的窄带乘积函数（Product Function,PF）作为重构信号;最后利用谱峭度方法和包络解调方法对重构信号进行处理。实验结果表明,噪声参数最优ELMD方法可以有效地抑制ELMD分解中的模态混叠,与谱峭度结合可以准确地提取滚动轴承故障特征。

Abstract

In order to extract fault features of rolling bearing precisely and steadily,a method of bearing fault diagnosis,which is based on optimal noise parameters ensemble local mean decomposition（ ELMD） and spectral kurtosis（ SK） is proposed. Firstly,the relative root-mean-square error is introduced to determine the amplitude of the optimal noise. Then,the fault signal is decomposed into a series of narrow band product functions（ PFs） by using optimal noise parameters ELMD method,and the product functions are obtained with having the highest correlation with the original vibration signal as the reconstructed signal. Finally,the method based on spectral kurtosis and envelope analysis is used to deal with the reconstructed signal. The experimental results indicate that the mode mixing can be restrained effectively by the optimal noise parameter ELMD method and fault features of rolling bearing can be extracted accurately by the approach based on optimal noise parameters ELMD and spectral kurtosis.

关键词

噪声参数最优总体局部均值分解谱峭度相对均方根误差模态混叠

Keywords

Optimal noise parameterELMDSpectral KurtosisRelative root-mean-square errorMode mixing

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