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Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

Hyungseob Han, Sangjin Cho, Sundeok Kwon and Sang-Bock Cho

In the fault diagnosis system using empirical mode decomposition (EMD), it is important to select the intrinsic mode functions (IMFs) which contain as much fault information as possible and to alleviate the problems of mode mixing and spurious modes. An e... see more

fault diagnosis | improved complete ensemble empirical mode decomposition | intrinsic mode function | neural network

Electronics , 2018

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

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 crea... see more

vehicle–bridge interaction | bridge identification | empirical mode decomposition | ensemble empirical mode decomposition natural frequency

Infrastructures , 2019

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Ensemble Empirical Mode Decomposition: Image Data Analysis with White-noise Reflection

M. Kopecký

During the last decade, Zhaohua Wu and Norden E. Huang announced a new improvement of the original Empirical Mode Decomposition method (EMD). Ensemble Empirical Mode Decomposition and its abbreviation EEMD represents a major improvement with great versati... see more

Empirical Mode Decomposition (EMD) | Ensemble Empirical Mode Decomposition (EEMD) | image analysis | sifting | Noise-Assigned Data Analysis (NADA)

Acta Polytechnica , 2010

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Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

Xiaoli Li and Chengwei Li

The noise of near-infrared spectra and spectral information redundancy can affect the accuracy of calibration and prediction models in near-infrared analytical technology. To address this problem, the improved Complete Ensemble Empirical Mode Decompositio... see more

near-infrared spectra | wavelength selection | improved CEEMDAN | energy entropy | permutation entropy

Entropy , 2017

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Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Taiyong Li, Zhenda Hu, Yanchi Jia, Jiang Wu and Yingrui Zhou

Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However,... see more

crude oil prices | time series forecasting | ensemble empirical mode decomposition (EEMD) | sparse Bayesian learning (SBL) | energy forecasting

Energies , 2018

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An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow

Jonathan T. Barge, Hatim O. Sharif

This study focused on employing Linear Genetic Programming (LGP), Ensemble Empirical Mode Decomposition (EEMD), and the Self-Organizing Map (SOM) in modeling the rainfall–runoff relationship in a mid-size catchment. Models were assessed with regard to the... see more

Water , 2016

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A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Huimin Zhao, Meng Sun, Wu Deng and Xinhua Yang

Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extract... see more

Entropy , 2017

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An EMD-Based Chaotic Least Squares Support Vector Machine Hybrid Model for Annual Runoff Forecasting

Xuehua Zhao, Xu Chen, Yongxin Xu, Dongjie Xi, Yongbo Zhang, Xiuqing Zheng

Accurate forecasting of annual runoff is necessary for water resources management. However, a runoff series consists of complex nonlinear and non-stationary characteristics, which makes forecasting difficult. To contribute towards improved prediction accu... see more

empirical mode decomposition | chaotic characteristics | phase space reconstruction | least squares support vector machine | runoff forecasting

Water , 2017

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A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network

Bangzhu Zhu

Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble fore... see more

Energies , 2012

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