Fast Ensemble Empirical Mode Decomposition Research Articles

Carbon market price prediction based on sequence decomposition-reconstruction-dimensionality reduction and improved deep learning model

Identifying carbon market price signals and accurately predicting carbon prices is of great significance in the development of carbon markets and carbon trading management. In this study, a fast ensemble empirical mode decomposition (FEEMD) method was used to analyze the fluctuation patterns of carbon market prices and obtain their characteristic components. Then the characteristic components of carbon prices were reconstructed using sample entropy theory (SRSE), and a partial autocorrelation analysis (PACF) technique was used to screen the data points exerting large impacts on carbon market prices. The FEEMD-SRSE-PACF-ILSTM hybrid model constructed. The main innovation points are as follows: The FEEMD is proposed to solves the common modal aliasing problem during carbon price decomposition and improves the efficiency of the carbon price sequence decomposition; the sample entropy theory and partial autocorrelation analysis methods are applied to reconstruct the characteristic components of carbon prices; the LSTM neural network model was optimized by the grey wolf algorithm to enhance its stability and adaptability. The analyzed price changes of large regional carbon markets in China, the European Union, and the United States can be effectively used to predict prices in the carbon market and guide its participants towards low-carbon investment and trading.

Measurement of health evolution tendency for aircraft engine using a data-driven method based on multi-scale series reconstruction and adaptive hybrid model

With the improvement of structure complexity and the strict requirement for stable operation, maintenance pattern of aircraft engine has experienced the transformation from passive response to active prevention. Accurate result of health evolution trend measuring is the core part of conducting the prevention maintenance. In this paper, a data-driven method based on multi-scale series reconstruction and adaptive hybrid model is proposed to measure the health development tendency of aircraft engine. Firstly, to quantitatively characterize the health levels of engine, a comprehensive health state index (CHSI) is innovatively constructed by an improved ensemble auto-encoder (EnAE) and self-organizing map (SeOM) neural network, which realizes the feature-level fusion of multi-source sensory data. By designing novel network structure and training pattern in EnAE, original auto-encoder model is optimized and the more robust state features can be captured from raw signals. Secondly, considering the influence of series random fluctuations on forecasting results, a multi-scale intrinsic mode functions (IMFs) reconstruction strategy using the fast ensemble empirical mode decomposition based dispersion entropy (FEEMD-DE) theory is provided to efficiently extract the regular and irregular components from original CHSI series. Finally, an adaptive hybrid model, combining a recurrent reconstructed Grey Markov (RRGM) model and long short-term memory (LSTM) network, is developed to capture the complex characteristics of reconstruction components and then complete the measurement of health evolution trend. The feasibility and superiority of the proposed method is validated by using a multi-source sensory dataset collected from aircraft engines, and the experimental results indicate that the measuring accuracy of the proposed method is significantly higher than that of other existing methods.

Improved Brain-Computer Interface Signal Recognition Algorithm Based on Few-Channel Motor Imagery.

Common spatial pattern (CSP) is an effective algorithm for extracting electroencephalogram (EEG) features of motor imagery (MI); however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor—even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels (three channels), wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass filtered EEG into multiple time–frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time–frequency components and the original EEG signal. Furthermore, phase space reconstruction (PSR) was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster–Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II (left- and right-hand MI), which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels.

Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model

Accompanying the trend of low-carbon energy consumption, natural gas has ushered in the energy transition era’s development. However, rapid growth has thrown off the balance of urban natural gas supply and demand, resulting in gas shortages in many Chinese cities for several consecutive years. This work proposes a novel model for short-term load forecasting that combined the decomposition-fusion technique with a replacement data function, feature selection, and a diversified Stacking ensemble learning model. First, fast ensemble empirical mode decomposition is used to disintegrate the original complex nonstationary time series data into several modes. To ensure accurate information and computational efficiency while preventing excessive decomposition, the Pearson coefficient is used to fuse highly correlated patterns further. Second, hybrid feature engineering is used to select high contribution candidate input variables. Finally, K-Flod cross-validation is performed in each base-learner to enhance generalization capability during the training process. The empirical results prove that the base-learners’ capabilities and discrepancy will significantly impact the model (MAE = 167.409, MAPE = 3.125, RMSE = 234.654). Even if different types of city data are used, the proposed model’s effectiveness and robustness in gas load forecasting is not weakened, and decomposition-fusion technology can boost the model’s effectiveness. However, it cannot play a decisive role; the ensemble learning approach can integrate the ascendancy of the single model while effectively reducing the risk of falling into a local minimum. The developed model has good application prospects in natural gas dispatch and control systems as it outperforms alternative models in prediction accuracy, adaptability, stability, and generalization ability.

A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China

Wind power is recognized as one of the most promising renewable and clean energy sources under the context of the increasing depletion of fossil fuels. The exact wind speed forecasting has great significance for the large-scale connection of wind farms with the power grid. In light of this, this paper contributes to establishing a novel hybrid model that can predict the future wind speed accurately. Firstly, the original wind speed time series is decomposed by the fast ensemble empirical mode decomposition into several sub-series that are further integrated by the runs test. The phase space reconstruction is used to dynamically choose each integrated sub-series' input and output vectors for the prediction model. Additionally, an improved whale optimization algorithm is exploited to optimize the weights and bias of the extreme learning machine. Finally, prediction results are obtained from the aggregation of each integrated sub-series prediction. To verify the accuracy and applicability of the proposed hybrid model, we apply several comparative models to conducted two case studies using different wind speed time series from Inner Mongolia that is Asia's largest gathering area of wind power farms. According to the experimental results, it can be concluded that the decomposition reduces the volatility and randomness of wind speed, and the runs test lowers the forecasting complexity. The phase space reconstruction can capture the chaotic property of wind speed series. The optimization for the whale optimization algorithm enhances its global and local optimization ability to further improve the performance of extreme learning machine. Overall, the proposed hybrid model can effectively capture the non-linear characteristics of wind speed series.

Fast Ensemble Empirical Mode Decomposition Using the Savitzky Golay filter

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.

New State Identification Method for Rotating Machinery under Variable Load Conditions Based on Hybrid Entropy Features and Joint Distribution Adaptation

Fault identification under variable operating conditions is a task of great importance and challenge for equipment health management. However, when dealing with this kind of issue, traditional fault diagnosis methods based on the assumption of the distribution coherence of the training and testing set are no longer applicable. In this paper, a novel state identification method integrated by time-frequency decomposition, multi-information entropies, and joint distribution adaptation is proposed for rolling element bearings. At first, fast ensemble empirical mode decomposition was employed to decompose the vibration signals into a collection of intrinsic mode functions, aiming at obtaining the multiscale description of the original signals. Then, hybrid entropy features that can characterize the dynamic and complexity of time series in the local space, global space, and frequency domain were extracted from each intrinsic mode function. As for the training and testing set under different load conditions, all data was mapped into a reproducing space by joint distribution adaptation to reduce the distribution discrepancies between datasets, where the pseudolabels of the testing set and the final diagnostic results were obtained by the k-nearest neighbor algorithm. Finally, five cases with the training and testing set under variable load conditions were used to demonstrate the performance of the proposed method, and comparisons with some other diagnosis models combined with the same features and other dimensionality reduction methods were also discussed. The analysis results show that the proposed method can effectively recognize the multifaults of rolling element bearings under variable load conditions with higher accuracies and has sound practicability.

An intelligent approach for predicting resource usage by combining decomposition techniques with NFTS network

Time sensitive virtual machines that run real-time control tasks are constrained by hard timing requirements. Optimal resource management and guarantee the hard timing requirements of virtual machines are critical goals. Basically, cloud resource usage predicting and resource reservation play a crucial role to achieve these two goals. So, we propose a predicting approach based on two-phase decomposition method and hybrid neural network to predict future resource usage. This paper uses a clustering method based on the AnYa algorithm in an on-line manner in order to obtain the number of fuzzy rules and the initial value of the premise and consequent parameters. Since cloud resource usage varies widely from time to time and server to server, extracting the best time series model for predicting cloud resource usage depend not only on time but on the cloud resource usage trend. For this, we present a recursive hybrid technique based on singular spectrum analysis and adaptively fast ensemble empirical mode decomposition to identify the hidden characteristics of the time series data. This method tries to extract seasonal and irregular components of the time series. According to the simulation results, it can be found that the proposed model can have significantly better performance than the three comparison models from one-step to six-step CPU usage predictions with the MAPE of 33.83% average performance promotion, MAE of 36.54% average performance promotion, RMSE of 36.70% average performance promotion.

Hourly PM2.5 concentration forecasting based on mode decomposition-recombination technique and ensemble learning approach in severe haze episodes of China

Accurate prediction of PM2.5 concentration for its monitoring and early warning has become the major concern in terms of frequent occurrences of severe haze episodes in China. In this paper, a novel model for hourly PM2.5 concentration prediction during severe haze episodes is suggested, where mode decomposition-recombination technique and ensemble learning approach are innovatively introduced into high concentration PM2.5 forecasting. Firstly, the fast ensemble empirical mode decomposition (FEEMD) is applied to decompose the original complex non-stationary data into several modes with different frequencies to offset the impact of data noise. The sample entropy (SE) is used to further recombine similar modes aiming at avoiding excessive-decomposition to promote accurate information extraction and computational efficiency. Next, the partial autocorrelation analysis (PACF) is executed on the recombined modes to select appropriate input features of the forecasting model. Finally, the Stacking-driven ensemble model (SDEM) is developed as the forecasting model to enhance the feature representation and information utilization capacities in which K-fold cross-validation is carried out in each base-model to enhance the generalization performance. Outputs of each base-models are new input for the meta-model to acquire ultimate superior forecasting values. The empirical results of Baoding during autumn and winter illustrate that: (1) the proposed model exhibits effectiveness and robustness in PM2.5 concentration prediction, particularly, these superiorities will not be weakened even at extremely high concentration points of PM2.5; (2) the decomposition-recombination technique is potential to handle nonlinear and high volatility data; (3) the ensemble learning approach can inherit and integrate merits of single models; (4) the developed FEEMD-SE-SDEM model outperforms other contrast models in terms of forecasting accuracy, stability and class prediction correct rate, which is promising in early air quality warning system.

New fault diagnosis approaches for detecting the bearing slight degradation

In this paper, two new methods for detecting the bearing’s degradation starting points are presented based on the vibration signal analysis. In the first method, a new feature extraction technique is suggested based on the envelope harmonic-to-noise ratio (EHNR) and the fast ensemble empirical mode decomposition (FEEMD). Each vibration signal is decomposed into its intrinsic mode functions (IMFs) using the FEEMD algorithm. Also, a novel technique has been introduced based on the autocorrelation function (ACF) of the original signal and its IMFs for selecting the most appropriate IMF and eliminate the noisy components. Then, the EHNR of the most sensitive IMF is computed for detecting the early degradation of bearing. In the second method, a new adaptive feature is defined using the ACF of the raw signal and the energy-entropy vector. At first, a novel indicator called the periodicity intensity factor (PIF) is introduced using the energy of the ACF of the raw signal and its maximum points. In the next step, the energy-entropy variations of the PIF factor are investigated for recognizing the fault starting point in bearings. In this work, the vibration signals of the run-to-failure experiment are used to appraise the presented techniques. The results indicate that the proposed approaches are able to detect the exact moment of the defect occurrence. Also, comparing the results of this paper with other techniques presented recently indicates the superiority of the proposed approaches.

A novel compound wind speed forecasting model based on the back propagation neural network optimized by bat algorithm.

Wind power, a clean and renewable resource, is regarded as one of the most promising and economical resources during the transformation from fossil fuels to new energy resources. Thus, the accuracy of wind speed forecasting work is very important to integrate the wind resource into electrical power system on a large scale. To improve the short-term wind speed forecasting accuracy, a novel compound model is introduced in this paper. For the proposed model, the fast ensemble empirical mode decomposition method was employed to do the data preprocessing. After the data preprocessing, phase space reconstruction was used for choosing each sub-series' input and output vectors for the forecasting model dynamically. Then, the bat algorithm was applied to optimize the connection weights and thresholds of the traditional back propagation neural network. The forecasting results can be obtained through the aggregation of sequential prediction. The performance evaluation of this proposed model indicates that it can capture the nonlinear characteristics of the wind speed signal efficiently. The proposed model shows better performance when being compared with the parallel models.

An intelligent bearing fault diagnosis method based on the AFEEMD and 1D CNNs

To process the non-stationary vibration signals and improve accuracy of bearing fault diagnosis, this paper presents a novel intelligent fault diagnosis method based on the adaptive fast ensemble empirical mode decomposition (AFEEMD) and one-dimensional convolutional neural networks (1D CNNs). First, the AFEEMD algorithm is utilized to decompose the raw signals into intrinsic mode functions (IMFs). Then, the time and frequency statistic features of the first several IMFs are analyzed to form feature vector, which are used as the input of 1D CNNs to identify the bearing fault. The performance of the proposed method is validated using the dataset from the Case Western Reserve University (CWRU). Compared with the traditional back propagation neural network (BPNN), the results show that the proposed AFEEMD-1D CNNs method not only can obtain higher accuracy and achieve more reliable performance, but also can improve the generalization performance. Due to the end-to-end feature learning capacity, it can be extended to other machinery for fault diagnosis.

A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition.

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.

A hybrid wind speed forecasting model based on a decomposition method and an improved regularized extreme learning machine

Abstract Accurate wind speed forecasting plays a critical role in wind farm planning for economic operation of power grid. New forecasting methods which lead to better performances are urgently demanded for the wind energy management as there is no one model that is qualified in all cases. In this paper, a novel hybrid model is developed by integrating Fast Ensemble Empirical Mode Decomposition (FEEMD) and Regularized Extreme Learning Machine (RELM) optimized by Backtracking Search Algorithm (BSA) to realize the accurate short-term wind speed forecasting. This hybrid model can be partitioned into three phases: data preprocessing, training RELM by BSA, and aggregated calculation. Experiments have been conducted under different forecasting horizons and seasons with the proposed method as well as five benchmark models (RBF, GRNN, RELM, FEEMD-RELM, and BSA-RELM), respectively. Experimental results show the proposed model reduces RMSE by about 60% on average for all four cases than single RELM and it is superior to all the involved models in short-term wind speed forecasting.

Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machine

With the development of the carbon market in China, research on the carbon price has received more and more attention in related fields. However, due to its nonlinearity and instability, the carbon price is undoubtedly difficult to predict using a single model. This paper proposes a new hybrid model for carbon price forecasting that combines fast ensemble empirical mode decomposition, sample entropy, phase space reconstruction, a partial autocorrelation function, and an extreme learning machine that has been improved by particle swarm optimization. The original carbon price series is decomposed using the fast ensemble empirical mode decomposition and sample entropy methods, which eliminate noise interference. Then, the phase space reconstruction and partial autocorrelation function methods are combined to determine the input and output variables in the forecasting models. An extreme learning machine optimized by particle swarm optimization was employed to forecast carbon prices. An empirical study based on carbon prices in three typical regional carbon markets in China found that this new hybrid model performed better than other comparable models.

Degradation Tendency Measurement of Aircraft Engines Based on FEEMD Permutation Entropy and Regularized Extreme Learning Machine Using Multi-Sensor Data

As the core component and main power source for aircrafts, the reliability of an aero engine is vital for the security operation of aircrafts. Degradation tendency measurement on an engine can not only improve its safety, but effectively reduce the maintenance costs. In this paper, a hybrid method using multi-sensor data based on fast ensemble empirical mode decomposition permutation entropy (FEEMD-PE) and regularized extreme learning machine (RELM), systematically blending the signal processing technology and trend prediction approach, is proposed for aircraft engine degradation tendency measurement. Firstly, a synthesized degradation index was designed utilizing multi-sensor data and a data fusion technique to evaluate the degradation level of the engine unit. Secondly, in order to eliminate the irregular data fluctuation, FEEMD was employed to efficiently decompose the constructed degradation index series. Subsequently, considering the complexity of intrinsic mode functions (IMFs) obtained through sequence decomposition, a permutation entropy-based reconstruction strategy was innovatively developed to generate the refactored IMFs (RIMFs), which have stronger ability for describing the degradation states and contribute to improving the prediction accuracy. Finally, RIMFs were used as the inputs of the RELM model to measure the degradation tendency. The proposed method was applied to the degradation tendency measurement of aircraft engines. The results confirm the effectiveness and superiority of the proposed method, and it is more suitable for actual applications compared with other existing approaches.

A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

With the growing penetration of wind power into electric grids, improving wind speed prediction accuracy has become particularly valuable for the exploitation of wind power. In this paper, a novel hybrid strategy based on a three-phase signal decomposition (TPSD) technique, feature extraction (FE) and weighted regularized extreme learning machine (WRELM) is developed for multi-step ahead wind speed prediction. The TPSD including seasonal separation algorithm (SSA), fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) is proposed for the first time to handle the complex and irregular natures of wind speed comprehensively. The FE process is used to capture the useful features of wind speed fluctuations and determine the optimal inputs for a prediction model. The WRELM is employed as a basic predictor for building the prediction model by these selected features. Four real wind speed prediction cases are utilized to evaluate the proposed model, and experimental results verify the effectiveness of the proposed model compared with the benchmark models.

A Single-Channel Blind Source Separation Technique Based on AMGMF and AFEEMD for the Rotor System

To improve the accuracy of the diagnosis of the composite failure of the rotor system under underdetermined conditions, a single-channel blind source separation method based on an adaptive multiscale generalized morphology filter (AMGMF) and an adaptive fast ensemble empirical mode decomposition (AFEEMD) algorithm is proposed. First, AMGMF is used to filter out the background noise in the signal and enhance the signal-to-noise ratio (SNR), and then, the filtered signal is decomposed by the AFEEMD algorithm to improve the accuracy of signal decomposition and reduce computation time. Second, the singular value decomposition-based method is used to estimate the number of source signals, and the main components highly correlated with the observed signal are selected according to the number of source signals. Finally, the effective separation of source signals is achieved by the fast independent component analysis (FastICA) algorithm. Simulation tests and multiple rotor fault signals are used to demonstrate the feasibility and effectiveness of the proposed method. Experimental results show that the proposed method has better separation performance than the existing EEMD-PCA-FastICA method.

Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network

With the sharp consumption of fossil energy and deterioration of ecological environment, wind power, as a clean and renewable energy resource, gains more and more attention from all over the world. However, due to the intermittency and stochastic nature of wind speed, an accurate wind power/speed forecasting is not only crucial for availably dispatching the wind power resource but also has a direct relationship with the State Grid operation safely and steadily. In this paper, an innovative hybrid wind speed forecasting model, including fast ensemble empirical mode decomposition, sample entropy, phase space reconstruction and back-propagation neural network with two hidden layers, is proposed to enhance the accuracy of wind speed prediction. The data is firstly preprocessed by fast ensemble empirical mode decomposition and sample entropy. Subsequently, the prediction model called improved back-propagation neural network is built to forecast the sub-series, whose inputs and outputs are obtained in accordance to phase space reconstruction. To verify the effectiveness and advancement of the proposed model, the paper chooses the real data from two wind farms located in different site in China for experiments. Comparing with the benchmark models, the proposed model shows a better performance in short-term wind speed forecasting.

Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting

In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model.