经验模态分解在密封轴承故障诊断中的应用  

Sealed Bearing Fault Diagnosis Method Based on Empirical Mode Decomposition

作　　者：孔凡国[1] 张永孝[1] 宋剑虹[1] 

机构地区：[1]五邑大学机电工程学院,广东江门529020

出　　处：《机械设计与研究》2011年第3期70-72,90,共4页Machine Design And Research

基　　金：广东省自然科学基金资助项目(06029824)

摘　　要：应用Labview直观的图形化界面将采集到的有缺陷的轴承信号转换为数字信号,在labview中调用matlab函数程序.将经验模态分解(EMD)引入到轴承的振动特征信号提取中,再从若干个包括故障的IMF分量中提取能量特征参数以判别故障产生的部位。试验结果表明,经验模态分解的分析方法在判断轴承故障的部位时具有很高的准确性,是一种有效的轴承故障诊断方法。Using Labview graphical user interface the deiective bearing signals were collected and converted into digital signals, then called Matlab function in Labview. Applying the Matlab powerful data processing capabilities, the empirical mode decomposition （EMD） is introduced into the extraction of bearing vibration feature signal, then the energy feature parameters are extracted from a number of IMFs with main fault information which can be served as distin- guishing the location of fault. The results indicate that EMD has a very high accuracy when determining the location of bearing failure; so this is an effective bearing fault diagnosis method.

关 键 词：Labview和Matlab 经验模态分解 特征频率 包络谱 

分 类 号：TH115[机械工程—机械设计及理论]