CEEMD与FastICA结合的故障特征提取方法

METHOD OF FAULT FEATURE EXTRATION BASED ON CEEMD AND FASTICA

针对滚动轴承故障特征信息难以分离的问题,提出了互补式集成经验模态分解(Complementary Ensemble Empirical Mode Decomposition,CEEMD)与快速独立分量分析(Fast Independent Component Analysis,Fast ICA)结合的故障特征提取方法。首先将振动信号进行CEEMD分析,分解成不同尺度的固有模态分量(Intrinsic mode function,IMF);然后通过敏感度评估算法对分解信号进行重组,并利用Fast ICA对其进行降噪处理;最后对Fast ICA分离的信号进行Hilbert包络谱分析,获取故障特征信息。将此方法应用于滚动轴承振动信号故障分析,实验证明了所提方法的有效性。

In order to solve the problem that the fault feature information of rolling bearing is difficult to be separated,a new method of fault feature extraction is presented,which is based on the complementary ensemble empirical mode decomposition（ CEEMD） and fast independent component analysis（ Fast ICA）. First,analyze the CEEMD vibration signals,decompose them into intrinsic mode function（ IMF） components signal of different scales; then through the sensitivity evaluation algorithm,decompose and recombine the signals,and use Fast ICA to reduce their noise; in the end,conduct Hilbert envelope spectrum analysis to the signals separated by the Fast ICA,to obtain the fault feature information. This method is applied to the fault analysis of rolling bearing vibration signal,and was proved to be valid.