基于LMD近似熵和FCM聚类的机械故障诊断研究  

Study on mechanical fault diagnosis method based on LMD approximate entropy and fuzzy C-means clustering

作　　者：张淑清[1] 孙国秀[1] 李亮[1] 李新新[1] 监雄[1] 

机构地区：[1]燕山大学电气工程学院河北省测试计量技术及仪器重点实验室,秦皇岛066004

出　　处：《仪器仪表学报》2013年第3期714-720,共7页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(51075349;61077071);河北省自然科学基金(F2011203207)资助项目

摘　　要：提出一种基于局部均值分解(local mean decomposition,LMD)近似熵和模糊C均值聚类(fuzzy C-means clustering,FCM)相结合的机械故障诊断方法。首先对机械振动信号进行LMD分解,得到若干具有物理意义的乘积函数(product function,PF)分量,再通过相关性分析,筛选出与原始信号相关性最大的3个分量作为数据源,求取其近似熵作为特征向量,最后通过FCM模糊聚类对特征向量进行识别分类。实验表明,基于LMD近似熵和FCM模糊聚类相结合的方法对机械故障信号能够有效准确地进行识别分类,此外,将该方法与基于EMD近似熵和FCM结合的方法进行对比,结果表明该方法具有更好的故障识别效果。A new approach for mechanical fault diagnosis based on local mean decomposition （LMD） approximate entropy and fuzzy C-means clustering （FCM） is proposed. Firstly, fault mechanical vibration signal is decomposed with LMD to obtain a certain number of product function （PF） components that have physical meaning. With correlation analysis, three PF components that have largest correlation coefficients with the original signal are sifted out and used as the data source. The approximate entropies of these three PF components are calculated and used as the eigenvectors. At last, the constructed eigenveetors are put into FCM classifier to recognize different fault types. The results of experiment and engineering analysis demonstrate that the method based on local mean decomposition （LMD） approximate entropy and fuzzy c-means clustering is able to diagnose mechanical faults accurately and effectively. In addition, compared with the method based on empirical mode decomposition （EMD）approximate entropy and FCM, the proposed approach could obtain better fault identification result.

关 键 词：局部均值分解 模糊C均值聚类 近似熵 故障诊断 

分 类 号：TH17[机械工程—机械制造及自动化]