基于最大相关波形延拓改进的EEMD方法

发布时间：2018-11-21 16:35

【摘要】：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。
[Abstract]:In order to solve the problem of endpoint effect and modal aliasing in empirical mode decomposition (empirical mode decomposition,EMD), an improved method of aggregate empirical mode decomposition (ensemble empirical mode decomposition,EEMD) based on maximum correlation waveform continuation is proposed. The maximum correlation waveform method is used to extend the two ends of the original signal to realize the smooth transition of the extension data at the boundary of the original signal and to reduce the fitting error of the envelope at the end of the signal. To solve the problem that parameters can not be obtained automatically in EEMD, adaptive EEMD is used to decompose the new signal to improve the precision of signal decomposition. The simulation analysis and rotor unbalance fault diagnosis example show that the improved EEMD method can not only reduce the false modal components and effectively suppress the phenomenon of modal aliasing, but also improve the decomposition distortion caused by the endpoint effect. At the same time, compared with the improved methods based on extreme point symmetry continuation and image extension, the proposed method has higher resolution accuracy.
【作者单位】：天津工业大学电子与信息工程学院;天津工业大学天津市光电检测技术与系统重点实验室;
【基金】：国家自然科学基金(61405144) 天津市自然科学基金(15JCQNJC42100) 天津市科技特派员项目(16JCTPJC48100,16JCTPJ47200)~~
【分类号】：TN911.7

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