基于EEMD的ICA算法在轴承-丝杠复合故障诊断中的应用  

Applications of ICA Algorithm Based on EEMD in Bearing-Screw Composite Fault Diagnosis

作　　者：李善[1] 谭继文[1] 俞昆 

机构地区：[1]青岛理工大学机械工程学院,山东青岛266520

出　　处：《机床与液压》2016年第23期160-163,共4页Machine Tool & Hydraulics

基　　金：国家自然科学基金资助项目(51075220);山东省高等学校科技计划项目(J13LB11);高等学校博士学科点专项科研基金(20123721110001);青岛市科技计划基础研究项目(12-1-4-4-(3)-JCH)

摘　　要：提出了一种基于EEMD的ICA算法,旨在解决单通道轴承-丝杠复合故障的信号分离。首先通过EEMD分解,将复合信号分解在不同的通道中,得到一系列IMF分量;再计算各IMF的峭度值和相关系数值,选取数值较大的几个IMF分量,与原始信号重新组成一组观测信号,作为ICA的输入,得到一系列IC分量;最后选取含有冲击成分较大的IC分量,进行包络分析,对故障类型进行诊断识别。通过实验成功分离并识别出两种故障类型,证明了该方法的有效性。The Independent component analysis （ICA） algorithm was proposed based on Ensemble empirical mode decomposition （EEMD） , which aims to solve the bearing-screw composite fault signal separation in single channel. First of all, through the EEMD method, the composite signal was decomposed in different channels to get a series of component of the IMF. Then the kurtosis value of the fund and phase relationship value was calculated to select some numerical larger IMF component. A set of observations was newly formed with the original signals, as input of the ICA to get a series of IC component. Lastly, selecting for impact ingredients of larger IC components, envelope analysis was carried out to diagnose the fault type identification. Through the experiment, separate and identify two types of fault successfully to prove the effectiveness of the proposed method.

关 键 词：总体平均经验模态分解(EEMD) 独立分量分析(ICA) 峭度指标 相关系数 复合故障 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化]