Morphological Filter-Assisted Ensemble Empirical Mode Decomposition. (17th September 2018)

• Record Type: Journal Article
• Authors: Zhou, Xiaohang; Shan, Deshan; Li, Qiao
• Other Names: Della Corte Alessandro Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2018(2018)
• Publication Date: 2018-09-17
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2018/5976589
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : In the ensemble empirical mode decomposition (EEMD) algorithm, different realizations of white noise are added to the original signal as dyadic filter banks to overcome the mode mixing problems of empirical mode decomposition (EMD). However, not all the components in white noise are necessary, and the superfluous components will introduce additional mode mixing problems. To address this problem, morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) was proposed in this paper. First, a new method for determining the structuring element shape and size was proposed to improve the adaptive ability of morphological filter (MF). Then, the adaptive MF was introduced into EMD to remove the superfluous white noise components to improve the decomposition results. Based on the contributions of MF in a single EMD process, the MF-EEMD was proposed by combining EEMD with MF to suppress the mode mixing problems. Finally, an analog signal and a measured signal were used to verify the feasibility of MF-EEMD. The results show that MF-EEMD significantly mitigates the mode mixing problems and achieves a higher decomposition efficiency compared to that of EEMD.