基于非线性小波变换和经验模式分解的轴承故障特征提取  

Fault Feature Extraction of Transmission Bearing via Nonlinear Wavelet Transform and Empirical Mode Decomposition

作　　者：陈祥龙[1] 胡春艳 梅检民[3,4] 肖云魁[3] 贾继光[5] 

机构地区：[1]军事交通学院研究生管理大队,天津300161 [2]总装备部通保部,北京100081 [3]军事交通学院汽车工程系,天津300161 [4]军械工程学院火炮工程系,石家庄050003 [5]71897部队,信阳464194

出　　处：《军事交通学院学报》2011年第11期78-82,共5页Journal of Military Transportation University

摘　　要：针对滚动轴承等复杂零部件振动信号故障特征难于提取的特点,采集4组变速器轴承振动信号,构造基于第二代小波变换的非线性小波变换对振动信号进行自适应预处理,有效地去除了振动信号中的噪声,抑制了经验模式分解过程中的模态混叠。将预处理后的信号进行经验模式分解,获得了一系列瞬时频率且具有物理意义的本征模函数,对每个本征模函数进行Hilbert变换,得到了振动信号的Hilbert边际谱,提取出了信号能量随瞬时频率变化的特征。For complex parts such as bearings are difficult to extract the fault characteristic of vibration signal,collecting 4 groups transmission bearing vibration signals and using nonlinear wavelet transform via second generation wavelet transform to preprocess these signals.This method effectively eliminated the noise of vibration signals and restrained EMD decomposition mode mixing,obtaining intrinsic mode function components which instantaneous frequency has physical meanings.By executing Hilbert transform to every IMF component,the signals＇ Hilbert marginal spectrums are obtained,and the signals＇ instantaneous parameters are extracted.

关 键 词：非线性小波变换 经验模式分解 故障特征提取 HILBERT变换 

分 类 号：U463.212[机械工程—车辆工程]