基于EMD和小波包的轴承故障特征提取  

Extracting bearing fault based on EMD and Wavelet Packet

作　　者：张毅[1] 

机构地区：[1]长治学院电子信息与物理系,山西长治046011

出　　处：《信息与电子工程》2012年第3期330-333,338,共5页information and electronic engineering

摘　　要：提出了一种将经验模态分解(EMD)和小波包(WP)分解相结合的方法,提取某些电机信号非平稳特征。首先对电机振动信号作EMD分解,再对其分解结果单模态函数作WP包络谱分析,从而得到精确度较高的轴承内圈故障频率。最后,通过仿真和实例,将本方法和已有文献中的方法进行对比,结果表明,该方法不仅具有较高的可行性,而且可以准确地提取出故障信息。A method is proposed to extract non-stationary parameters of some motors by combining Empirical Mode Decomposition（EMD） and Wavelet Packet（WP） transform. First, EMD is performed on motor non-stationary signal, then the decomposed results, named intrinsic mode functions, are applied with WP envelope spectrum analysis to obtain the fault frequency with higher accuracy. The method is compared with other methods by simulation and example test, and it is proved that the proposed method not only features higher feasibility, but also can extract fault information more accurately.

关 键 词：经验模态分解 小波包变换 故障提取 

分 类 号：TN911.7[电子电信—通信与信息系统]