基于流形学习和改进VPMCD的滚动轴承故障诊断方法  

The rolling bearings fault diagnosis method based on Manifold learning and improved VPMCD

作　　者：潘海洋[1,2] 杨宇[1] 李永国[1] 程军圣[1] 

机构地区：[1]湖南大学汽车车身先进设计制造国家重点实验室,湖南长沙410082 [2]安徽工业大学机械工程学院,安徽马鞍山243032

出　　处：《振动工程学报》2014年第6期934-941,共8页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(51175158,51075131);湖南省自然科学基金资助项目(11JJ2026);中央高校基本科研业务费专项基金资助项目(531107040301)

摘　　要：提出一种基于拉普拉斯特征映射流形学习算法(Laplacian Eigenmaps,简称LE)和改进多变量预测模型(Variable predictive model based class discriminate,简称VPMCD)的滚动轴承故障诊断方法,首先对振动信号进行局部特征尺度分解(Local characteristic scale decomposition,简称LCD),并提取各内禀尺度分量(Intrinsic scale component,简称ISC)的特征构造高维特征向量,接着采用LE算法挖掘出高维数据中包含有效信息且具有内在规律性的低维特征,然后输入到基于Kriging的改进多变量预测模型(Kriging-variable predictive model based class discriminate,简称KVPMCD)分类器中进行模式识别。该方法充分利用并有效结合了LCD在信号处理、LE在挖掘特征信息和KVPMCD在模式识别方面的优势,实现了滚动轴承故障特征提取到故障识别的全程诊断。实验分析结果表明:基于LE算法和KVPMCD的分类方法可以有效地对滚动轴承的工作状态和故障类型进行识别。A new method of rolling bearing fault diagnosis is proposed based on Laplacian Eigenmaps Manifold learning and improved Variable predictive model based class discriminate（VPMCD）,firstly,the vibration signals are decomposed using Local characteristic scale decomposition（LCD）,and extracting the feature of each Intrinsic scale component（ISC）to construct the high-dimensional eigenvectors;then using IE algorithm to dig low-dimensional eigenvectors that contain valid information and have intrinsic regularity in high dimensional data;finally,the low-dimensional eigenvectors are inputted Kriging-Variable predictive model based class discriminate（KVPMCD）classifier for pattern recognition.This method makes full use and effective combination of the advantages of LCD in the signal processing,LE in mining feature information,KVPMCD in pattern recognition,and realizes the entire diagnosis from rolling bearings fault feature extraction to fault identification.Experimental results show that the KVPMCD model based on the LE algorithm can effectively identify work status and fault type of rolling bearing.

关 键 词：故障诊断 滚动轴承 LE KVPMCD 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133.33