Fault diagnosis method of rolling element bearings based on EEMD,measure-factor and fast kurtogram
Fault diagnosis method of rolling element bearings based on EEMD,measure-factor and fast kurtogram
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发表时间：
2012
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通讯作者：
Xie Xiao-liang
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作者：
Xie Xiao-liang
在EMD、谱峭度和包络分析等信号处理方法用于滚动轴承故障诊断的基础上，提出了一种基于EEMD、测量因子和快速峭度图的改进方法：首先，采用集合经验模态分解（EEMD）将故障信号分解为一组本征模态函数（IMF）;该方法首先利用基于距离的测量因子，选取最能反映故障信息的IMF进行信号重构，然后利用快速峰图生成中心频率和带宽，构造最优带通滤波器，最后利用最优带通滤波器对故障信号进行滤波，得到最优的带通滤波器。通过比较滤波后信号的包络谱与滚动轴承故障特征频率，确定具体故障，仿真数据和内圈实测信号验证了该方法的有效性，具有良好的前景。
On the basis of fault diagnosis of rolling element bearings with signal processing methods,such as,EMD,spectral kurtosis and envelope analysis,an improved methodology based on EEMD,measure-factor and fast kurtogram was proposed.Firstly,fault signals were decomposed into a group of intrinsic mode functions(IMFs) with ensemble empirical mode decomposition(EEMD).Secondly,the IMF best representing the fault information was selected to reconstruct signals using the measure-factor based on distance.Thirdly,an optimal band-pass filter was constructed with the central frequency and bandwidth generated using the fast kurtogram.Finally,the specific fault was determined by comparing the envelope spectrum of the filtered signals with the fault characteristic frequency of rolling element bearings.The effectiveness of the proposed methodology was demonstrated with the simulated data and the actual signals measured of inner races,and the improved method had a good prospect for its application in rolling element bearing diagnosis.