经验模态分解和关联维数在机械故障诊断中的应用研究  

Empirical Mode Decomposition and Correlation Dimension Applied to Machine Fault Diagnosis

作　　者：张新广[1] 李志农[1] 王心怡[1] 

机构地区：[1]郑州大学机械工程学院,郑州450001

出　　处：《机床与液压》2008年第4期196-198,共3页Machine Tool & Hydraulics

基　　金：国家自然科学基金资助项目(50775208);河南省杰出人才创新基金资助项目(0621000500);河南省教育厅自然科学基金资助项目(2006460005)

摘　　要：结合经验模态分解(Empirical Mode Decomposition,EMD)和关联维数各自的优点,提出了一种基于EMD和关联维数相结合的机械故障诊断方法,该方法是先对采集到的非平稳振动信号进行EMD分解,对分解后的每一个平稳的本征模函数(Intrinsic Mode Function,IMF)分量使用G_P算法进行关联积分,并通过拟合函数求出关联维数。运用关联维数可以定性地分析非线性系统的特性,从而识别转子系统的故障类型。实验结果表明该方法是有效的。Combining the advantage of the Empirical Mode Decomposition（EMD） and Correlation Dimension,a machine fault diagnosis method based on EMD and correlation dimension was proposed.In the proposed method,the vibration signal is firstly decomposed into a finite number of stationary Intrinsic Mode Functions（IMF） by the EMD method,then every IMF correlation integral of every IMF is obtained respectively with G_P algorithm,and the correlation dimension is obtained by the fitting function.The characteristic of correlation dimension,which can analyse the nonlinearity system qualitatively,was used to identify the fault pattern of rotor system.The experiment result shows that the proposed method is very effective.

关 键 词：经验模态分解 关联维数 故障诊断 

分 类 号：TN911[电子电信—通信与信息系统] TH17[电子电信—信息与通信工程]