Mode Mixing Suppression Algorithm for Empirical Mode Decomposition Based on Self-Filtering Method

2019 ◽  
Vol 62 (9) ◽  
pp. 462-473
Author(s):  
Longwen Wu ◽  
Yupeng Zhang ◽  
Yaqin Zhao ◽  
Guanghui Ren ◽  
Shengyang He
2017 ◽  
Vol 09 (02) ◽  
pp. 1750004 ◽  
Author(s):  
Pawel Rzeszucinski ◽  
Michal Juraszek ◽  
James R. Ottewill

The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.


Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 53
Author(s):  
Jian Zhang ◽  
Rahul Soangra ◽  
Thurmon E. Lockhart

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.


Sci ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 39 ◽  
Author(s):  
Jian Zhang ◽  
Rahul Soangra ◽  
Thurmon E. Lockhart

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.


2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang

Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.


2012 ◽  
Vol 04 (01n02) ◽  
pp. 1250015 ◽  
Author(s):  
JOHN L. AVEN ◽  
ARNOLD J. MANDELL ◽  
RICHARD COPPOLA

We present a method for enhancing signals possessing nonlinear and nonstationary characteristics, which we call weighting functional-empirical mode decomposition (WF-EMD). The filtering method is based upon the empirical mode decomposition (EMD) and utilizes an energy-based weighting scheme to recombine the decomposed modes into a single cleansed version of the signal. The filter has been developed in such a way that no restrictive assumptions about the data are required. Furthermore, the temporal resolution of the data is left unaltered, as it would occur in many common data-smoothing methods. The design of this filter has been influenced by improving the calculation accuracy of dynamical measures, such as fractal dimensions and Lyapunov exponents, of neurodynamical recordings such as those obtained through electroencephalography (EEG) or magnetoencephalography (MEG).


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