Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal

Wang, Tong; Zhang, Mingcai; Yu, Qihao; Zhang, Huyuan

doi:10.1016/j.jappgeo.2012.05.002

Cited by 311 publications

(140 citation statements)

References 14 publications

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“…Due to the randomness of white noise, the additive noise can be reduced in the mean of the corresponding IMFs by sufficient repeated trials. EEMD has better performance than EMD [21,22] and has been widely applied [23][24][25].…”

Section: Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

Satellite Image Time Series Decomposition Based on EEMD

Yun-long

Meng

et al. 2015

Remote Sensing

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Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD) is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs). EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD). Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), and Global Environment Monitoring Index (GEMI) time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes.

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Section: Ensemble Empirical Mode Decompositionmentioning

confidence: 99%

Satellite Image Time Series Decomposition Based on EEMD

Yun-long

Meng

et al. 2015

Remote Sensing

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“…Compared with the wavelet decomposition, empirical mode decomposition is not affected by the selection of wavelet base and the number of decomposition layers, but based on the adaptability of data itself, and the noise reduction performance of load sequence is better. Based on the traditional EMD, Ensemble Empirical Mode Decomposition (EEMD) adopted Gauss white noise to reduce the generation of modal aliasing in a certain range [32,33]. Jiang et al [34] proposed a hybrid approach based on the ensemble empirical mode decomposition and grey support vector machine for short-term high-speed rail passenger flow forecasting.…”

Section: Introductionmentioning

confidence: 99%

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

2018

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Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm), is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM) is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM ( The experimental results fully demonstrate the reliability and effectiveness of the CEEMDAN-MGWO-SVM model proposed in this paper for daily peak load forecasting, which shows the strong generalization ability and robustness of the model. Keywords: daily peak load forecasting; complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); modified grey wolf optimization (MGWO); support vector machine (SVM)

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“…Wavelet and Fourier transform methods cannot accurately analyze vibration signals because of poor adaptation. The empirical mode decomposition (EMD) represents a classical time-frequency analysis method, and EMD has been widely adopted in mechanical fault diagnosis, earthquake monitoring, and bridge and constructions state monitoring [6][7][8]. However, EMD suffers from over-envelope, under-envelope, end-effect, and other shortcomings [9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

Liang

Dong-min

Daidi

et al. 2018

Shock and Vibration

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A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.

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Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal

Cited by 311 publications

References 14 publications

Satellite Image Time Series Decomposition Based on EEMD

Satellite Image Time Series Decomposition Based on EEMD

Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

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