基于最大相关波形延拓改进的EEMD方法

针对经验模态分解(empirical mode decomposition,EMD)中出现的端点效应和模态混叠现象问题,提出了利用最大相关波形延拓改进聚合经验模态分解(ensemble empirical mode decomposition,EEMD)方法.利用最大相关波形法对原始信号的两端进行延拓,实现延拓数据在原信号边界处的平滑过渡,减小端点处包络线的拟合误差.针对EEMD中参数无法自动获取的问题,采用自适应EEMD对新信号进行分解,提高信号的分解精度.通过仿真分析和转子不平衡故障诊断实例研究表明,改进的EEMD方法不仅能够明显减少虚假模态分量、有效抑制模态混叠现象,而且较好地改善了端点效应引起的分解失真问题.同时与基于极值点对称延拓改进方法及基于镜像延拓改进方法相比,所提方法具有较高的分解精度.

An improved EEMD method based on maximal correlation waveform extension

Aiming at the existing problems of end effects and mode mixing in EMD, an improved EEMD method based on MCWE(maximal correlation waveform extension) is proposed. Firstly, MCWE is used to achieve a smooth transition at the junction of an original signal and its extension and reduce the error of envelope near the ends of data. Secondly, in order to solve the problem that the parameters can not be obtained automatically in EEMD, the adaptive EEMD is used to decompose the new signal to improve the decomposition accuracy. Simulation results and rotor unbalance fault diagnosis examples show that the proposed new method not only can reduce false components and eliminate mode mixing in EMD, but also improve the decomposition distortion caused by end effect effectively. At the same time, it has higher decomposition accuracy compared with the improved EEMD method based on symmetrical extrema extension and the improved EEMD method based on mirror extension.