一种抑制EMD端点效应新方法及其在信号特征提取中的应用

A new method for restraining the end effect of empirical mode decomposition and its applications to signal feature extraction

经验模式分解(EMD)的端点效应一直是困扰其工程实用性的难点问题,结合端点效应产生机理和现有的研究成果,提出了一种基于端点优化对称延拓(End Optimization Symmetric Extension,EOSE)的抑制EMD端点效应新方法。通过对信号和其包络线的偏差评价函数的最小化计算,获取最佳的信号端点值,在此基础上延拓信号的上、下包络线将最大化地逼近原始信号两端点,在EMD后续"筛选"固有模态分量(IMF)过程中抛弃两端延拓的数据,将端点效应释放到原始信号的以外。将原EMD、基于EOSE改进EMD和基于时间序列建模(ARMA)改进EMD3种方法进行对比分析,仿真和实验结果表明,基于EOSE方法抑制EMD端点效应的效果最好,能够精确提取出旋转机械振动信号的典型故障特征,运算效率较高。

The end effect of empirical mode decomposition （EMD） was always a difficult and significant problem, based on the consideration of the mechanism of the end effect and the current research development, we presented a new method of End optimization symmetric extension, through which the end effect is restrained. Firstly, the assumption is that the two ends of original signal are unknown, and which is extended in a point-symmetrical manner with end-point as its center. Secondly, a deviation error evaluation function concerning original signal is constituted and minimized, then the two optimization endpoints value are obtained. Extended in a point symmetrical manner with new end-point value, the obtained up and down envelops maximally approached original signal end-point, which restrained the envelops divergence in EMD algorithm. Finally, in the process of filtering intrinsic mode function （IMF） discard the extended data, which release end effect to original signal outside and maximally reduced distortion of original signal. Compared the three methods named EMD, EMD based on EOSE and EMD based on ARMA, simulation and experiment show that the proposed method could restrain end effect effectively and precisely extract classic fault feature of vibration signal of rotating machine.