基于集成经验模态分解和峭度准则的滚动轴承故障特征提取方法

为实现滚动轴承故障的精确诊断,提出一种基于集成经验模态分解(ensemble empirical mode decomposition,EEMD)与峭度准则的包络解调方法。该方法首先利用EEMD将振动信号分解,然后利用峭度最大准则选取EEMD分解后的本征模函数(intrinsic mode function,IMF),将该本征模函数进行包络解调从而获得滚动轴承的故障特征信息。该方法可以有效抑制经验模态分解(empirical mode decomposition,EMD)中的模态混叠问题,同时还避免了共振解调方法中中心频率及滤波频带的选取,具有良好的自适应性。利用该包络解调方法对实际滚动轴承发生内圈、外圈故障进行了分析,证明了该方法可以有效地提取滚动轴承故障特征信息,能够实现滚动轴承故障的精确诊断。

Rolling Bearing Fault Feature Extraction Method Based on Ensemble Empirical Mode Decomposition and Kurtosis Criterion

In order to realize the precise fault diagnosis of rolling bearing,an envelope demodulation method was proposed based on ensemble empirical mode decomposition(EEMD) and kurtosis criterion.EEMD was used to decompose the vibration signal into several Intrinsic Mode Functions(IMFs) first,and then an envelope demodulation was adopted with the IMF which selected by the maximal kurtosis criterion to extract the fault information.This method can not only restrain the mode mixing phenomenon caused by empirical mode decomposition(EMD),but also avoid the selection of center frequency and filter band in resonance demodulation method,so it has good adaptability.On the basis of discussing inner and outer vibration fault mechanism of rolling bearing,the proposed method was used to analyze the vibration signal of the actual fault rolling bearings.The result shows that this method can efficiently extract the fault information and realize the precise fault diagnosis of rolling bearing.