Feature Extraction of Gearbox Early Fault based on ISGMD and MED

Shuzhou Dong; Xunpeng Qin; Shiming Yang

doi:10.16578/j.issn.1004.2539.2022.03.024

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Feature Extraction of Gearbox Early Fault based on ISGMD and MED

Development·Application | 更新时间：2022-09-24

- Feature Extraction of Gearbox Early Fault based on ISGMD and MED
- Journal of Mechanical Transmission Vol. 46, Issue 3, Pages: 154-162(2022)
- 作者机构：
  
  1.武汉理工大学汽车工程学院，湖北武汉 430070
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2022.03.024
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董书洲,秦训鹏,杨世明.基于ISGMD和MED的齿轮箱早期故障特征提取[J].机械传动,2022,46(03):154-162.

Dong Shuzhou,Qin Xunpeng,Yang Shiming.Feature Extraction of Gearbox Early Fault based on ISGMD and MED[J].Journal of Mechanical Transmission,2022,46(03):154-162.
董书洲,秦训鹏,杨世明.基于ISGMD和MED的齿轮箱早期故障特征提取[J].机械传动,2022,46(03):154-162. DOI： 10.16578/j.issn.1004.2539.2022.03.024.

Dong Shuzhou,Qin Xunpeng,Yang Shiming.Feature Extraction of Gearbox Early Fault based on ISGMD and MED[J].Journal of Mechanical Transmission,2022,46(03):154-162. DOI： 10.16578/j.issn.1004.2539.2022.03.024.

摘要

针对在强噪声背景下难以识别齿轮箱早期故障以及复合故障的问题，提出了一种改进辛几何模态分解（Improved symplectic geometry mode decomposition，ISGMD）和最小熵解卷积（Minimum entropy deconvolution，MED）相结合的故障特征提取方法。首先，将信号经最小熵解卷积预处理，突出信号中的故障冲击成分；然后，将故障增强信号通过改进辛几何模态分解自适应地分解为若干辛几何分量，并依据峭度最大准则选取峭度值最大的敏感辛几何分量；最后，对选定的敏感辛几何分量进行包络分析，从而有效地提取出齿轮箱的故障特征。通过实验，验证了该方法的有效性。

Abstract

Aiming at the difficulty in identifying early faults and compound faults of gearboxes under strong noise background，a method of extracting fault features based on the combination of improved symplectic geometry mode decomposition （ISGMD） and minimum entropy deconvolution （MED） is proposed. Firstly，the signal is preprocessed by minimum entropy deconvolution to highlight the fault impact component in the signal. Then，the fault enhancement signal is adaptively decomposed into several symplectic geometric components through improved symplectic geometric modal decomposition，and the sensitive symplectic geometric component with the largest kurtosis value is selected according to the maximum kurtosis criterion. Finally，an envelope analysis of the selected sensitive symplectic geometric components can effectively extract the fault features of the gearbox. The effectiveness of the method is verified by experimental.

关键词

齿轮箱辛几何模态分解最小熵解卷积早期故障特征提取

Keywords

GearboxSymplectic geometry mode decompositionMinimum entropy deconvolutionEarly faultFeature extraction

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