基于经验模态分解的机械故障欠定盲源分离方法  

Underdetermined blind source separation method of machine faults based on empirical mode decomposition

作　　者：李志农[1] 吕亚平[1] 范涛[1] 冷传广[2] 

机构地区：[1]郑州大学机械工程学院,郑州450001 [2]河南交通职业技术学院汽车工程系,郑州450005

出　　处：《航空动力学报》2009年第8期1886-1892,共7页Journal of Aerospace Power

基　　金：国家自然科学基金(50775208);河南省教育厅自然科学基金(2008C460003;2006460005)

摘　　要：针对传统的机械故障源分离方法限于非高斯、平稳和相互独立的源信号,且传感器的观测数多于源数,机械故障源信号通常不易满足这些假设的局限性,提出了一种基于经验模态分解的机械故障欠定盲源分离方法.该方法对混合观测信号进行经验模态分解,然后重组本征模函数分量作为新的观测信号进行盲源分离,仿真和实验结果表明,该方法是有效的.The traditional mechanical fault source separation method is restricted to nongaussian,stationary and mutually independent source signals,and the number of observations is assumed to be more than the number of sources.These deficiencies may cause many problems in the application of fault diagnosis,because fault signals generally do not meet these conditions.Based on these deficiencies,an underdetermined blind separation method of machine faults based on empirical mode decomposition(EMD) was proposed.The pro...

关 键 词：欠定盲分离 经验模态分解 故障诊断 时频分析 

分 类 号：TH17[机械工程—机械制造及自动化]