基于排列熵和VPMCD的滚动轴承故障诊断方法  

Rolling bearing fault diagnosis method based on permutation entropy and VPMCD

作　　者：程军圣[1] 马兴伟[1] 杨宇[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,长沙410082

出　　处：《振动与冲击》2014年第11期119-123,共5页Journal of Vibration and Shock

基　　金：国家自然科学基金(51175158;51075131);湖南省自然科学基金(11JJ2026)

摘　　要：VPMCD(Variable Predictive Model Based Class Discriminate)是一种新的模式识别方法,它充分利用从原始数据中所提取的特征值之间的相互内在关系建立数学模型,从而进行模式识别。论文将VPMCD结合排列熵(Permutation Entropy,简称PE)方法应用于滚动轴承故障诊断。首先采用ITD(Intrinsic Time-scale Decomposition,简称ITD)对滚动轴承振动信号进行分解,得到若干个固有旋转(Proper Rotation)分量,并对包含主要故障信息的PR分量提取排列熵作为故障特征值;然后,对VPMCD分类器进行训练;最后,采用VPMCD分类器进行故障识别和分类。实验数据的分析结果表明该方法能够有效地应用于滚动轴承故障诊断。Variable predictive model-based class discriminate （VPMCD） is a new pattern recognition method,it makes full use of inner relations among characteristic values extracted from the original data to recognize models.Here,VPMCD was combined with permutation entropy （PE） to diagnose rolling bear faults.Firstly,rolling bearing vibration signals were adaptively decomposed into a sum of proper rotation （PR） components by using ITD and the permutation entropies of PR components containing the main faults information were extracted as characteristic values of faults.Secondly,the characteristic values were used to train the parameters of VPMCD.Finally,the VPMCD classifier was used to recognize and classify the faults.The experimental results showed that this method can be effectively applied to diagnose rolling bearing faults.

关 键 词：VPMCD ITD 排列熵 滚动轴承 故障诊断 

分 类 号：TH113[机械工程—机械设计及理论]