An Optimal Ensemble Empirical Mode Decomposition Method for Vibration Signal Decomposition

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Shi-Chang Du ◽  
Tao Liu ◽  
De-Lin Huang ◽  
Gui-Long Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Decomposition ◽  
Critical Parameters ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Two Parameters ◽  
Mixing Problem ◽  
Mode Mixing

The vibration signal decomposition is a critical step in the assessment of machine health condition. Though ensemble empirical mode decomposition (EEMD) method outperforms fast Fourier transform (FFT), wavelet transform, and empirical mode decomposition (EMD) on nonstationary signal decomposition, there exists a mode mixing problem if the two critical parameters (i.e., the amplitude of added white noise and the number of ensemble trials) are not selected appropriately. A novel EEMD method with optimized two parameters is proposed to solve the mode mixing problem in vibration signal decomposition in this paper. In the proposed optimal EEMD, the initial values of the two critical parameters are selected based on an adaptive algorithm. Then, a multimode search algorithm is explored to optimize the critical two parameters by its good performance in global and local search. The performances of the proposed method are demonstrated by means of a simulated signal, two bearing vibration signals, and a vibration signal in a milling process. The results show that compared with the traditional EEMD method and other improved EEMD method, the proposed optimal EEMD method automatically obtains the appropriate parameters of EEMD and achieves higher decomposition accuracy and faster computational efficiency.

scholarly journals On the Use of Ensemble Empirical Mode Decomposition for the Identification of Bridge Frequency from the Responses Measured in a Passing Vehicle

2019 ◽  
Vol 4 (2) ◽  
pp. 32 ◽  
Author(s):  
Licheng Zhu ◽  
Abdollah Malekjafarian
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Noise ◽  
Profile Measurement ◽  
Fe Method ◽  
The Road ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Mixing Problem ◽  
Mode Mixing

In this paper, ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods are used for the effective identification of bridge natural frequencies from drive-by measurements. A vehicle bridge interaction (VBI) model is created using the finite element (FE) method in Matlab. The EMD is employed to decompose the signals measured on the vehicle to their main components. It is shown that the bridge component of the response measured on the vehicle can be extracted using the EMD method. The influence of some factors, such as the road roughness profile and measurement noise, on the results are investigated. The results suggest that the EMD shows good performance under those conditions, but the accuracy of the results may still need to be improved. It is shown that in some cases, the EMD may not be able to decompose the signal effectively and includes mode mixing. This results in inaccuracies in the identification of bridge frequencies. The use of the ensemble empirical mode decomposition (EEMD) method is proposed to overcome the mode mixing problem. The influence of factors such as road profile, measurement noise and vehicle velocity are investigated. It is numerically demonstrated that employing the EEMD improves the results compared to the EMD.

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.

An Enhanced Extraction Method Based on EEMD for Processing a Bearing Vibration Signal With Multiple Vibration Sources

2014 ◽  
Author(s):  
Wei Guo
Keyword(s):  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Similarity Criterion ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Combination Technique ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Eemd Method ◽  
Effective Signal

Condition monitoring and fault diagnosis for rolling element bearings is an imperative part for preventive maintenance procedures and reliability improvement of rotating machines. When a localized fault occurs at the early stage of real bearing failures, the impulses generated by the defect are relatively weak and usually overwhelmed by large noise and other higher-level macro-structural vibrations generated by adjacent machine components and machines. To indicate the bearing faulty state as early as possible, it is necessary to develop an effective signal processing method for extracting the weak bearing signal from a vibration signal containing multiple vibration sources. The ensemble empirical mode decomposition (EEMD) method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different bands of simple signal components. However, the energy dispersion and many redundant components make the decomposition result obtained by the EEMD losing the physical significance. In this paper, to enhance the decomposition performance of the EEMD method, the similarity criterion and the corresponding combination technique are proposed to determine the similar signal components and then generate the real mono-component signals. To validate the effectiveness of the proposed method, it is applied to analyze raw vibration signals collected from two faulty bearings, each of which involves more than one vibration sources. The results demonstrate that the proposed method can accurately extract the bearing feature signal; meanwhile, it makes the physical meaning of each IMF clear.

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

scholarly journals Quantification of Dynamic Properties of Pile Using Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Feng Xiao ◽  
Gang S. Chen ◽  
Wael Zatar ◽  
J. Leroy Hulsey
Keyword(s):  
Empirical Mode Decomposition ◽  
Dynamic Properties ◽  
Ensemble Empirical Mode Decomposition ◽  
Frozen Soil ◽  
Frozen Ground ◽  
Mode Decomposition ◽  
Eemd Method ◽  
Pile Response ◽  
Emd Method ◽  
Mode Mixing

This paper investigated dynamical interactions between pile and frozen ground by using the ensemble empirical mode decomposition (EEMD) method. Unlike the conventional empirical mode decomposition (EMD) method, EEMD is found to be able to separate the mode patterns of pile response signals of different scales without causing mode mixing. The identified dynamic properties using the EEMD method are more accurate than those obtained from conventional methods. EEMD-based results can be used to reliably and accurately characterize pile-frozen soil interactions and help designing infrastructure foundations under permafrost condition.

Spin flight mode identification with OEEMD algorithm

2019 ◽  
Vol 91 (4) ◽  
pp. 582-600
Author(s):  
S. Abolfazl Mokhtari ◽  
Mehdi Sabzehparvar
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Complex Signals ◽  
Content Type ◽  
Mode Decomposition ◽  
Flight Mode ◽  
Highly Nonlinear ◽  
Mixing Problem ◽  
Mode Mixing

Purpose The paper aims to present an innovative method for identification of flight modes in the spin maneuver, which is highly nonlinear and coupled dynamic. Design/methodology/approach To fix the mode mixing problem which is mostly happen in the EMD algorithm, the authors focused on the proposal of an optimized ensemble empirical mode decomposition (OEEMD) algorithm for processing of the flight complex signals that originate from FDR. There are two improvements with the OEEMD respect to the EEMD. First, this algorithm is able to make a precise reconstruction of the original signal. The second improvement is that the OEEMD performs the task of signal decomposition with fewer iterations and so with less complexity order rather than the competitor approaches. Findings By applying the OEEMD algorithm to the spin flight parameter signals, flight modes extracted, then with using systematic technique, flight modes characteristics are obtained. The results indicate that there are some non-standard modes in the nonlinear region due to couplings between the longitudinal and lateral motions. Practical implications Application of the proposed method to the spin flight test data may result accurate identification of nonlinear dynamics with high coupling in this regime. Originality/value First, to fix the mode mixing problem in EMD, an optimized ensemble empirical mode decomposition algorithm is introduced, which disturbed the original signal with a sort of white Gaussian noise, and by using white noise statistical characteristics the OEEMD fix the mode mixing problem with high precision and fewer calculations. Second, by applying the OEEMD to the flight output signals and with using the systematic method, flight mode characteristics which is very important in the simulation and controller designing are obtained.

scholarly journals Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Yifan Li ◽  
Jianxin Liu ◽  
Yan Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Railway Wheel ◽  
Mode Decomposition ◽  
Track Irregularity ◽  
Wheel Flat ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing ◽  
Fault Characteristic

This study explores the capacity of the improved empirical mode decomposition (EMD) in railway wheel flat detection. Aiming at the mode mixing problem of EMD, an EMD energy conservation theory and an intrinsic mode function (IMF) superposition theory are presented and derived, respectively. Based on the above two theories, an improved EMD method is further proposed. The advantage of the improved EMD is evaluated by a simulated vibration signal. Then this method is applied to study the axle box vibration response caused by wheel flats, considering the influence of both track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method is verified by a test rig experiment. Research results demonstrate that the improved EMD can inhibit mode mixing phenomenon and extract the wheel fault characteristic effectively.

scholarly journals Prediction and analysis of non-stationary runoff extreme sequence based on ESMD combination prediction model

2020 ◽  
Vol 20 (4) ◽  
pp. 1439-1452
Author(s):  
Yi-zhen Li ◽  
Chun-fang Yue
Keyword(s):  
Prediction Model ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Stationary Time Series ◽  
Support Vector ◽  
Model Based ◽  
Mode Decomposition ◽  
Runoff Series ◽  
Mixing Problem ◽  
Mode Mixing

Abstract With increasingly severe climate changes and intensified human activities, it is more and more difficult to predict the non-stationary extreme runoff series accurately. In this research, based on the ‘decomposition-prediction-reconstruction’ model, an instantaneous frequency distribution map was used to measure the effect of empirical mode decomposition (EMD), ensemble empirical mode decomposition, complete ensemble empirical mode decomposition and extreme-point symmetric mode decomposition (ESMD) in dealing with mode mixing; appropriate prediction methods for each component were selected to form a combined prediction model; and the advantages of a combined prediction model based on ESMD were compared and analyzed with the following results acquired: (1) ESMD can address the mode mixing problem with EMD; (2) particle swarm optimization-least squares support vector machine, autoregressive model (1) and random forest are suitable for high-/medium-/low-frequency components and the residual components R; (3) the results of the combined prediction model are better than those of the single ones; and (4) the prediction effect of the combined prediction model is the best under ESMD decomposition, and the prediction errors of the runoff extreme value sequence can be reduced by about 58–80% compared with the three other decomposition methods. Moreover, as demonstrated in this study, the combined prediction model based on ESMD can effectively predict the non-stationary extreme runoff series, while providing reference for forecasting other non-stationary time series.

Application of MSE-EEMD Method in Gear Fault Diagnosis

2014 ◽  
Vol 530-531 ◽  
pp. 261-265
Author(s):  
Min Qiang Xu ◽  
Yong Bo Li ◽  
Hai Yang Zhao ◽  
Si Yang Zhang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Engineering Application ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Multiscale Entropy ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Eemd Method

Focus on the nonlinear and non-stationary characteristics of gear box vibration signal, the method of gear fault diagnosis based on Ensemble Empirical Mode Decomposition (EEMD) and multiscale entropy (MSE) was proposed . The complicated signal can be decomposed into several stationary IMF components with reality meanings by EEMD which has the advantages of eliminating aliasing state of vibration signal, and the MSE can extract the fault feature from the signals effectively. The concepts of EEMD and MSE are introduced firstly, and then they are applied to measure the complexity of gearbox signals. Through the engineering application of the diagnosis on gear typical fault of different wearing degree demonstrated that the proposed method can extracting the fault feature of gear fault effectively and realize the gear fault diagnosis.

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