Morphological Filter-Assisted Ensemble Empirical Mode Decomposition

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.

A Morphology Filter-Assisted Extreme-Point Symmetric Mode Decomposition (MF-ESMD) Denoising Method for Bridge Dynamic Deflection Based on Ground-Based Microwave Interferometry

“…Figure 4 shows the compared results of the curves between the original signal X(t) and the denoised signal S(t). Moreover, to validate the accuracy and robustness of the proposed MF-ESMD method for signal denoising, morphological filter and MF-EEMD methods [27,29] were selected for comparison with the proposed MF-ESMD method. e comparison results of the curves between the original signal X(t) and the denoised signal using the morphological filter and the MF-EEMD methods are shown in Figures 5(a) and 5(d).…”

“…is method changes the extremum distribution of the original signal by adding white Gaussian noise and further decomposes the original signal to alleviate the mode-mixing effect using the EMD method [21,[24][25][26][27][28][29][30]. However, the EEMD method is easily affected by the amplitude of the added noise and the number of ensemble trials, which can bring out some residual noise in the decomposed IMFs.…”

“…Compared with traditional linear filtering, morphological filtering is more efficient to a certain extent, and it overcomes the shortcomings of linear filtering in which some primary features of the original signal are harmed or blurred while smoothing the noise. us, the main morphological features of each signal remain more intact [29,36]. Moreover, a morphological filter is more effective in reducing impulse noise and white noise, and it will not cause an abrupt change in the frequency domain or a phase delay in the time domain [29,43].…”

“…us, the main morphological features of each signal remain more intact [29,36]. Moreover, a morphological filter is more effective in reducing impulse noise and white noise, and it will not cause an abrupt change in the frequency domain or a phase delay in the time domain [29,43]. To address modemixing problems, Zhou et al [29] proposed a morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) for signal denoising that could significantly mitigate the mode-mixing problems and achieve a higher decomposition efficiency compared to the EEMD method.…”

Data Inspecting and Denoising Method for Data‐Driven Stochastic Subspace Identification

“…For DRM, it is hard to determine a general standard for selecting the components of valuable structural response, and its low computational efficiency is another serious drawback. Recently, morphological filter (MF), which is a kind of timedomain filter, is being widely applied because of its high efficiency and capacity of considering nonlinearity [20,21]. MF was used by Zou and Liu to get a low distorted image for the target recognizing system, and it was proved that MF was superior to the traditional LDF [22].…”