基于极值符号序列分析的EMD端点效应处理方法

针对经验模式分解(empirical mode decomposition,简称EMD)的端点效应提出一种新的抑制方法。考虑到极值序列在EMD分解的包络线形成中占有主导地位,将信号局部极值序列进行符号化,根据符号特征进行特征匹配,在信号两端依据符号序列特征匹配结果进行符号序列拓延与对应信号还原,对拓延还原后的信号进行EMD分解以实现端点效应抑制。所提方法对于随机信号与周期信号都有着明显的抑制效果,通过对仿真信号和轴承故障信号端点效应的分析验证了方法的正确性。研究与ARMA模型、BP神经网络、镜像拓延等常见方法进行了对比,所提方法的各分量有效值指标均值为19.64%,低于其他方法,说明对低频分量有着更好的抑制效果。

Method of Empirical Mode Decomposition End Effect Based on Analysis of Extreme Value Symbol Sequence

An improved method for empirical mode decomposition (EMD) is proposed to solve the end effect in empirical mode decomposition. The sequence of extreme value is symbolized and taken as main characteristics by considering the importance of extreme values in EMD. The characteristics of extreme value are taken to match the sequence and restored to the original signal. In addition, EMD and Hilbert Huang are transformed with broadened signal to eliminate the endpoint effect. This method is assessed by both simulated signal and engineering signal and evaluated, significantly restraining the effect of extreme points at both periodic and non-periodic signals, based on analysis of the variation of extreme values. The proposed method is compared with the ARMA model, BP neural network and mirroring broaden, respectively. The average RMS of each component is 19.64%, which is lower than that of other methods. The findings demonstrate that the proposed method provides a better way to restrain the low-frequency component.