scholarly journals Morphological Filter-Assisted Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaohang Zhou ◽  
Deshan Shan ◽  
Qiao Li
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Measured Signal ◽  
Morphological Filter ◽  
Structuring Element ◽  
Mode Decomposition ◽  
Additional Mode ◽  
Adaptive Ability ◽  
Mode Mixing

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.

scholarly journals The Measurement and Elimination of Mode Splitting: From the Perspective of the Partly Ensemble Empirical Mode Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Liu ◽  
Peng Zheng ◽  
Qilin Dai ◽  
Zhongli Zhou
Keyword(s):  
Spectral Analysis ◽  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Mode Splitting ◽  
Mode Decomposition ◽  
Microseismic Signal ◽  
Mode Mixing

The problems of mode mixing, mode splitting, and pseudocomponents caused by intermittence or white noise signals during empirical mode decomposition (EMD) are difficult to resolve. The partly ensemble EMD (PEEMD) method is introduced first. The PEEMD method can eliminate mode mixing via the permutation entropy (PE) of the intrinsic mode functions (IMFs). Then, bilateral permutation entropy (BPE) of the IMFs is proposed as a means to detect and eliminate mode splitting by means of the reconstructed signals in the PEEMD. Moreover, known ingredient component signals are comparatively designed to verify that the PEEMD method can effectively detect and progressively address the problem of mode splitting to some degree and generate IMFs with better performance. The microseismic signal is applied to prove, by means of spectral analysis, that this method is effective.

scholarly journals Fast Ensemble Empirical Mode Decomposition Using the Savitzky Golay filter

2021 ◽  
Vol 1 (1) ◽  
pp. 79-86
Author(s):  
WAHIBA MOHGUEN ◽  
RAÏS EL’HADI BEKKA
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Computing Time ◽  
Ensemble Empirical Mode Decomposition ◽  
Mixing Effect ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Stationary Signals ◽  
Very High ◽  
Mode Mixing

Empirical mode decomposition (EMD) is a powerful algorithm proposed to analysis of nonlinear and non-stationary signals. The phenomenon of mode mixing is one of the major disadvantages of the EMD. The Ensemble EMD (EEMD) was introduced to eliminate the mode-mixing effect. The principle of EEMD is to add additional white noise into the signal with many trials. The noise in each trial is different; and the added noise can be completely cancelled out on average, if the number of trials is very high. The number of trials is a high computational load. The improvement on computational efficiency of EEMD is therefore required. In this paper, an improvement on the computing time of the EEMD was proposed by replacing white noise with white noise filtered using Savitzky-Golay (SG) filter. Numerical simulations were performed to demonstrate that such replacement has effectively reduced the number of trials to obtain a noise-free reconstructed signal.

Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging

2017 ◽  
Vol 09 (02) ◽  
pp. 1750004 ◽  
Author(s):  
Pawel Rzeszucinski ◽  
Michal Juraszek ◽  
James R. Ottewill
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Diagnostic Information ◽  
Optimal Parameters ◽  
Vibration Data ◽  
Mode Decomposition ◽  
Ultrasound Signal ◽  
Diagnostic Capabilities ◽  
Mixing Problem ◽  
Mode Mixing

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.

EEMD-Based Speaker Emotional Analysis for Speech Signal

2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang
Keyword(s):  
Emotion Recognition ◽  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
New Method ◽  
Speech Signals ◽  
Emotional Speech ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Mixing

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.

scholarly journals Analysis of Vehicle Platform Vibration Based on Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chengwu Shen ◽  
Zhiqian Wang ◽  
Chang Liu ◽  
Qinwen Li ◽  
Jianrong Li ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Measurement Accuracy ◽  
Vibration Isolation ◽  
Principal Component ◽  
Ensemble Empirical Mode Decomposition ◽  
Vibration Data ◽  
Mode Decomposition ◽  
Operation Process ◽  
Vehicle Platform ◽  
Mode Mixing

Vehicle platform vibration (VPV) directly affects the measurement accuracy of precise measuring instrument (PMI) fixed on it. In order to reduce the influences of VPV on measurement accuracy, it is necessary to perform vibration isolation between vehicle platform and PMI. Analysis of vibration characteristics is a prerequisite for vibration isolation. However, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) reveal that there is obvious mode mixing phenomenon in the collected VPV signals. In this paper, a noise stretch ensemble empirical mode decomposition (NSEEMD) method is proposed to suppress mode mixing, and the specific operation process of NSEEMD is expounded. By NSEEMD, mode mixing of the collected platform vibration data is well suppressed, and the principal component of platform vibration can be obtained.

Improved EEMD Applied to Rotating Machinery Fault Diagnosis

2011 ◽  
Vol 128-129 ◽  
pp. 154-159 ◽  
Author(s):  
Lue Chen ◽  
Ge Shi Tang ◽  
Yan Yang Zi ◽  
Fei Fan
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Precise Diagnosis ◽  
Empirical Mode Decomposition Method ◽  
Eemd Method ◽  
Simulation Signal ◽  
Improved Eemd

Ensemble Empirical Mode Decomposition (EEMD) is a new noise-assisted data analysis (NADA) method. The effect of EEMD depends on two key parameters which are the amplitude of white noise and the ensemble times. However, the shortcoming of EEMD is that it lacks adaptability and reliability because these two key important parameters are obtained by experience and human intervention. An Improved Ensemble Empirical Mode Decomposition method is proposed in this paper, by adding white noise and ascertaining ensemble number adaptively. The criterion of adding white noise in Improved EEMD is established, by which a composite simulation signal could be adaptively and accurately decomposed into IMFs without mode mixing. The proposed method is applied to a gear fault detection of hot strip finishing mills. The result shows that Improved EEMD method successfully extracts the gear fault feature with high precise diagnosis results.

scholarly journals A New Islanding Detection Technique using Ensemble Empirical Mode Decomposition

2020 ◽  
Vol 9 (3) ◽  
pp. 461-466
Keyword(s):  
Empirical Mode Decomposition ◽  
Distributed Generation ◽  
Gaussian White Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Islanding Detection ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Signal Processing Methods ◽  
Mixing Problem ◽  
Mode Mixing

Penetration of distributed generation (DG) is rapidly increasing but their main issue is islanding. Advanced signal processing methods needs a renewed focus in detecting islanding. The proposed scheme is based on Ensemble Empirical Mode Decomposition (EEMD) in which Gaussian white noise is added to original signal which solves the mode mixing problem of Empirical mode decomposition (EMD) and Hilbert transform is applied to obtained Intrinsic mode functions(IMF). The proposed method reliably and accurately detects disturbances at different events

Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Jing Yuan ◽  
Zhengjia He ◽  
Jun Ni ◽  
Adam John Brzezinski ◽  
Yanyang Zi
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Measured Signal ◽  
Accelerometer Data ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD

2009 ◽  
Vol 01 (01) ◽  
pp. 1-41 ◽  
Author(s):  
ZHAOHUA WU ◽  
NORDEN E. HUANG
Keyword(s):  
Data Analysis ◽  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Analysis Method ◽  
True Solution ◽  
New Approach ◽  
Time Frequency ◽  
Time Space ◽  
Mode Decomposition

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm. This new approach utilizes the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose; therefore, it represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA) method.

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