基于互补集合平均经验模态分解的滚动轴承故障诊断方法研究

Research on Fault Diagnosis Method of Rolling Bearing Based on Complementary Ensemble Empirical Mode Decomposition

针对集合经验模态分解(EEMD)的机械故障诊断方法中存在的不足,即其加入的白噪声不能完全被中和。为了克服其不足,提出了基于互补集合经验模态分解(CEEMD)的滚动轴承故障诊断方法,提出的方法很好地克服了EEMD中存在的不足,有效地消除了IMF中的残留噪声。仿真结果表明:提出的方法明显优于EEMD方法,可以减少重构误差,提取较为准确的IMF分量。最后,将CEEMD方法应用到滚动轴承故障诊断中,实验结果表明,CEEMD方法能准确的提取滚动轴承的特征故障频率。

In the mechanical fault diagnosis method based on ensemble empirical mode decomposition(EEMD),the added white noise is always not completely neutralized.In order to overcome this deficiency,a fault diagnosis method of rolling bearing based on complementary ensemble empirical mode decomposition(CEEMD)is proposed.The proposed method can overcome the shortcomings in the EEMD,and eliminate the residual noise in the IMF effectively.The simulation results show that the proposed method is superior to the EEMD method,the proposed method can reduce the reconstruction error and extract the more accurate IMF components.Finally,the proposed CEEMD method is applied to the fault diagnosis of rolling bearing.The experimental results show that the proposed method can effectively extract the characteristic frequency of rolling bearing fault.