集合经验模式分解在旋转机械故障诊断中的应用

为了抑制经验模式分解中的模式混淆现象，提高分析精度，引入集合经验模式分解（EEMD）算法。在分析信号上叠加适当的随机高斯白噪声序列，改变信号的局部时间跨度，从而改变一次经验模式分解（EMD）中分析的特征尺度，通过足够多次EMD分解，相当于从多个角度提取信号的本质，最后由所有次分解得出的各本征模态函数（IMF）的均值作为输出，不但消除了人为噪声的影响，还清晰还原了信号的内在过程，准确揭示了其真实物理意义。通过仿真试验和实际的动静碰磨故障案例证实了EEMD算法的有效性，并与基本EMD算法和高频谐波法进行了对比，结果表明，EEMD虽然耗时较多但结果更准确，在旋转机械故障诊断领域应用前景广泛。

Application of ensemble empirical mode decomposition in failure analysis of rotating machinery

For suppressing the phenomenon of mode mixing in empirical mode decomposition (EMD) and increasing the analysis accuracy, an improved algorithm named ensemble empirical mode decomposition (EEMD) was presented. A moderate Gauss white noise generated randomly was added to the original signal, which changed the local time span of the signal and rendered the analysis scales of EMD in a trial. By sufficient trials, considering as extracting the nature of the signal from different aspects, an ensemble mean of certain intrinsic mode function (IMF) decomposed by the EMD method was output as the final result of the new algorithm. The IMF eliminated bad effects of artificial noise, and indicated clearly the intrinsic processes of the signal with full of real meanings. EEMD method was validated by both simulation experiment and real rub-impact case, and then was compared with basic EMD algorithm and high-frequency-harmonic method. The results showed that EEMD was more precise but a little time-consuming, EEMD has good prospects of application in failure analysis of rotating machinery.