基于经验模式分解和广义维数的机械故障诊断  

Mechanical fault diagnosis based on empirical mode decomposition and generalized dimension

作　　者：郝研[1] 王太勇[1,2] 万剑[2] 张攀[2] 刘路[1] 

机构地区：[1]天津大学精密仪器与光电子工程学院,天津300072 [2]天津大学机械工程学院,天津300072

出　　处：《吉林大学学报（工学版）》2012年第2期392-396,共5页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(50975193);国家科技重大专项(2009ZX04014-101-05)

摘　　要：结合经验模式分解方法和广义维数的特点,提出了基于经验模式分解和广义维数的机械故障诊断方法。先对信号进行经验模式分解,得到内禀模态函数,然后求每个内禀函数的广义维数,从中提取盒维数、信息维数和关联维数,组成广义维数矩阵。通过分析待检测信号和各样本信号的广义维数矩阵相关系数,判断故障状态。实验结果表明,该方法可以准确地识别机械故障状态,是一种有效的机械故障诊断手段。A method of mechanical fault diagnosis based on empirical mode decomposition （EMD） and generalized dimension was presented. The intrinsic mode function （IMF） was obtained from the EMD of the signal, and the generalized dimension was derived from every IMF. The box dimension, the information dimension, and the correlation dimension were extracted from the generalized dimension to make up the generalized dimension matrix. Fault diagnosis was implemented by analyzing the correlation factors of generalized dimension matrix between target signal and samples. The experiment results showed that this method with accurate fault recognition was efficient for fault diagnosis.

关 键 词：机械故障 故障诊断 经验模式分解 广义维数 内禀模态函数 

分 类 号：TN911.72[电子电信—通信与信息系统]