Morphological Filter-Assisted Ensemble Empirical Mode Decomposition

Xiaohang Zhou,Deshan Shan,Qiao Li

doi:10.1155/2018/5976589

Xiaohang Zhou, Deshan Shan + Show 1 more

Open Access

https://doi.org/10.1155/2018/5976589

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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.

Highlights

Signal decomposition techniques, such as Fourier transform (FT), wavelet transform (WT), and empirical mode decomposition (EMD), are widely used in the field of structural health monitoring for denoising, parameter identification, and damage detection [1,2,3]
Flandrin and Rilling [11] noted that the white noise added in ensemble empirical mode decomposition (EEMD) could be taken as a set of dyadic filter banks, and different frequency components in the original signal were projected to the corresponding banks
Based on the above results, an improved morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) method was proposed by combining the EEMD with morphological filter (MF)

Summary

Introduction

Signal decomposition techniques, such as Fourier transform (FT), wavelet transform (WT), and empirical mode decomposition (EMD), are widely used in the field of structural health monitoring for denoising, parameter identification, and damage detection [1,2,3]. To overcome the mode mixing problem of EMD, Wu and Huang [10] proposed the noise-assisted ensemble empirical mode decomposition (EEMD). In the EEMD algorithm, EMD is performed repeatedly with the addition of different white noises, and an ensemble of the same order intrinsic mode functions (IMF) is made to obtain the final decomposition results. Flandrin and Rilling [11] noted that the white noise added in EEMD could be taken as a set of dyadic filter banks, and different frequency components in the original signal were projected to the corresponding banks. MF-EEMD is compared with EEMD by decomposing an analog signal and a measured signal, and the effectiveness of MF-EEMD is validated

MF Theory

Adaptive MF

MF-EEMD

Application Instance

Conclusion