Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition

Huili Chai; Meitao Ye

doi:10.16578/j.issn.1004.2539.2019.08.024

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Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition

Test·Analysis | 更新时间：2022-09-25

- Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition
- Journal of Mechanical Transmission Vol. 43, Issue 8, Pages: 130-134(2019)
- 作者机构：
  
  1.山西交通职业技术学院车辆工程系，山西太原 030031
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.08.024
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柴慧理,叶美桃.改进局部均值分解的齿轮箱复合故障特征提取[J].机械传动,2019,43(08):130-134.

Chai Huili,Ye Meitao.Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition[J].Journal of Mechanical Transmission,2019,43(08):130-134.
柴慧理,叶美桃.改进局部均值分解的齿轮箱复合故障特征提取[J].机械传动,2019,43(08):130-134. DOI： 10.16578/j.issn.1004.2539.2019.08.024.

Chai Huili,Ye Meitao.Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition[J].Journal of Mechanical Transmission,2019,43(08):130-134. DOI： 10.16578/j.issn.1004.2539.2019.08.024.

摘要

在强噪声环境下，针对局部均值分解（Local Mean Decomposition，LMD）出现的模态混叠现象，提出了总体局部均值分解方法（Ensemble Local Mean Decomposition，ELMD），但ELMD中所添加的白噪声不能完全被中和，这会导致PF分量受到所加白噪声的影响，导致重构误差增大。因此，提出基于PE-CELMD（Permutation Entropy-Complementary Ensemble Local Mean Decomposition）的齿轮箱复合故障诊断方法，该思路是在ELMD的基础上通过添加成对白噪声再结合排列熵（Permutation Entropy，PE）的方法优化LMD。将该方法应用于仿真信号和实测信号，并通过与LMD、CELMD对比，结果表明，PE-CELMD方法是一种有效的复合故障特征提取方法。

Abstract

In the case of strong noise, Ensemble local mean decomposition (ELMD) is proposed for the modal aliasing phenomenon of local mean decomposition（LMD）. However, the white noise added in ELMD cannot be completely neutralized, which will result in the reconstruction error increases due to the Product functions（PF）components to be affected by the added white noise. Therefore, a compound fault feature extraction method for gearbox based on PE-CELMD(Permutation Entropy-Complementary Ensemble local mean decomposition) is proposed. The idea is to optimize ELMD by adding pairwise white noise in combination with Permutation Entropy (PE) method based on ELMD. The method is applied to the simulated signal and the measured signal, and compared with LMD and CELMD, the results show that the PE-CELMD method is an effective compound fault feature extraction method.

关键词

局部均值分解排列熵复合故障

Keywords

Local mean decompositionPermutation entropyCompound fault

references

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