Fault Diagnosis of Rolling Bearings based on SVD-LMD Joint De-noising and TEO

Xiaozheng Xie; Jun Li; Rongzhen Zhao; Zhenqi Cui

doi:10.16578/j.issn.1004.2539.2021.06.016

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Fault Diagnosis of Rolling Bearings based on SVD-LMD Joint De-noising and TEO

Test·Analysis | 更新时间：2022-11-10

- Fault Diagnosis of Rolling Bearings based on SVD-LMD Joint De-noising and TEO
- Journal of Mechanical Transmission Vol. 45, Issue 6, Pages: 104-112(2021)
- 作者机构：
  
  1.兰州理工大学机电工程学院，甘肃兰州 730050
  2.甘肃筑鼎建设有限责任公司，甘肃嘉峪关 735100
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2021.06.016
  CLC：
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谢小正,李俊,赵荣珍等.SVD-LMD联合降噪和TEO的滚动轴承故障诊断[J].机械传动,2021,45(06):104-112.

Xie Xiaozheng,Li Jun,Zhao Rongzhen,et al.Fault Diagnosis of Rolling Bearings based on SVD-LMD Joint De-noising and TEO[J].Journal of Mechanical Transmission,2021,45(06):104-112.
谢小正,李俊,赵荣珍等.SVD-LMD联合降噪和TEO的滚动轴承故障诊断[J].机械传动,2021,45(06):104-112. DOI： 10.16578/j.issn.1004.2539.2021.06.016.

Xie Xiaozheng,Li Jun,Zhao Rongzhen,et al.Fault Diagnosis of Rolling Bearings based on SVD-LMD Joint De-noising and TEO[J].Journal of Mechanical Transmission,2021,45(06):104-112. DOI： 10.16578/j.issn.1004.2539.2021.06.016.

摘要

针对随机噪声背景下滚动轴承局部损伤信息提取困难的问题，提出了一种奇异值分解（Singular value decomposition，SVD）和局部均值分解（Local mean decomposition，LMD）联合降噪，并结合Teager能量算子（Teager energy operator，TEO）的特征提取新方法。首先，利用SVD方法对滚动轴承故障振动信号进行处理，初步剔除背景噪声；然后，使用LMD方法分解降噪后的信号，依据相关系数指标筛分出敏感乘积函数（Product function，PF）并加以重构；最后，对重构的信号进行TEO解调分析，将解调谱中幅值突出的频率成分与故障特征频率理论值进行对比，提取故障信息。结果表明，该方法可有效提取轴承局部损伤的特征频率，最终实现故障诊断。

Abstract

Aiming at the difficulty extracting the local damage information of rolling bearings under the background of random noise，a new feature extraction method based on singular value decomposition(SVD) and local mean decomposition(LMD) joint de-noising combined with Teager energy operator(TEO) is proposed. Firstly，by using the SVD method，the fault vibration signal of rolling bearings is processed to eliminated the background noise preliminarily. Then，the signal which is denoised by using LMD method is reconstructed after the sensitive product function(PF) is screened out according to the correlation coefficient index. Finally，the reconstructed signal is analyzed by TEO demodulation，the frequency component which amplitude prominent in demodulation spectrum is compared with the theoretical value of fault characteristic frequency to extract fault information. The experimental results demonstrate that the method can effectively extract the characteristic frequency of the local damage information of rolling bearings and the fault diagnosis is realized.

关键词

滚动轴承奇异值分解局部均值分解Teager能量算子故障诊断

Keywords

Rolling bearingSingular value decomposition(SVD)Local mean decomposition （LMD）Teager energy operator(TEO)Fault diagnosis

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