Research of the Gear Fault Diagnosis based on Improved LMD and Manifold Learning

Shen Chao; Yang Jianwei; Yao Dechen; Li Xi

doi:10.16578/j.issn.1004.2539.2018.01.029

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Research of the Gear Fault Diagnosis based on Improved LMD and Manifold Learning

更新时间：2022-10-20

- Research of the Gear Fault Diagnosis based on Improved LMD and Manifold Learning
- Vol. 42, Issue 1, Pages: 137-142(2018)
- 作者机构：
  
  1. 北京建筑大学机电与车辆工程学院
  2. 北京建筑大学城市轨道交通车辆服役性能保障北京重点实验室
  3. 北京市地铁运营有限公司地铁运营技术研发中心
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.01.029
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[1]沈超,杨建伟,姚德臣,李熙.基于改进局部均值分解和流形学习的齿轮故障诊断研究[J].机械传动,2018,42(01):137-142.

Shen Chao, Yang Jianwei, Yao Dechen, et al. Research of the Gear Fault Diagnosis based on Improved LMD and Manifold Learning. [J]. 42(1):137-142(2018)
[1]沈超,杨建伟,姚德臣,李熙.基于改进局部均值分解和流形学习的齿轮故障诊断研究[J].机械传动,2018,42(01):137-142. DOI： 10.16578/j.issn.1004.2539.2018.01.029.

Shen Chao, Yang Jianwei, Yao Dechen, et al. Research of the Gear Fault Diagnosis based on Improved LMD and Manifold Learning. [J]. 42(1):137-142(2018) DOI： 10.16578/j.issn.1004.2539.2018.01.029.

摘要

为更有效地利用齿轮振动信号进行故障诊断,提出基于改进局部均值分解（Local Mean Decomposition,LMD）和流形学习（ISOMAP）的齿轮故障特征提取方法。该方法将局部均值分解、模糊熵（Fuzzy Entropy,FE）和流形学习相结合。首先,利用LMD对原始振动信号进行多尺度分解,并在原LMD方法上添加自适应匹配波形以缓解端点效应对分解结果的影响;然后,对LMD分解后得到的乘积函数（Product Function,PF）进行模糊熵计算,获得原始信号不同尺度下的模糊熵数值,组成高维特征向量;最后,利用ISOMAP对高维特征向量进行二次特征提取,得到低维向量,进行故障识别。实际齿轮实验数据的处理结果表明该方法可以有效的诊断辨别齿轮故障,具有一定的优势。

Abstract

In order to diagnosis gear fault efficiently by using vibration signal,a new method of gear fault based on local mean decomposition（LMD）,fuzzy entropy and Isomap extraction is proposed,this method combines LMD,fuzzy entropy and Isomap. Firstly,by using the local mean decomposition（LMD） to decomposed the original vibration signal to obtain the components in different scales,and increases the adaptive matching waveform to alleviate the influence of end effects on decomposition results in the original LMD method. Then,considering fuzzy entropy can be use to distinguish the complexity of the signal effectively,so the fuzzy entropy of Product functions（PF） by LMD is calculated,a high-dimensional feature vector can be obtain with product functions. Finally,by using manifold learning（ISOMAP） on the high dimensional feature into low dimensional features which have better discrimination to describe different gear fault. It is applied to the gear experiment,the experimental results show that the method can effectively diagnose the gear faults and has certain superiority.

关键词

齿轮故障局部均值分解模糊熵流形学习ISOMAP

Keywords

Gear faultLocal mean decompositionFuzzy entropyManifold learningISOMAP

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