EMD Decomposition Research Articles

Influence of Coal Gangue Volume Mixing Ratio on the System Contact Response When Multiple Coal Gangue Particles Impacting the Metal Plate and the Study of Coal Gangue Mixing Ratio Recognition Based on the Metal Plate Contact Response and the Multi-Information Fusion

In order to clearly grasp the influence rules of the coal gangue volume mixture ratio on the system contact response, and finally realize the coal gangue mixing ratio recognition according to the contact response differences, on the basis of the Hertz contact theory and Flores nonlinear spring-damping contact theory, this paper proposed and established the theoretical model when multiple coal gangue particles elastic impacting the metal plate for the first time simultaneously in an undisturbed way with the consideration of the particles micro-compression, the macro-deflection of the metal plate and the functional transformation of the system. To further study the system response, the simulation when multiple coal gangue particles with the variable mixing ratio impacting the metal plate was carried out with consideration of the coal gangue brittleness and the elastic-plastic of metal plate. Then, broken response of coal gangue particles and the contact response of the metal plate under the different coal gangue mixing ratios were studied. After that, EMD decomposition and time-frequency domain parameters calculation were conducted for the vibration acceleration signal of the metal plate, and the influence law of gangue mixing ratio on the vibration acceleration signal time-frequency domain parameters of the metal plate was obtained. Finally, in line accordance with the self-proposed data standardization processing method, Kalman filtering, multi-information fusion method as well as the simulation data, the recognition of coal gangue mixture ratio based on the vibration acceleration signal multi-time-frequency domain parameter fusion and the recognition of coal gangue mixture ratio based on the metal plate multi-response information fusion were marched respectively, and the coal gangue mixture ratio multi-dimension recognition strategy based on Kalman filter and the energy fusion is proposed. The research results show that the proposed recognition method can greatly improve the recognition rate and the applicability of the identification strategy.

A note on sign-changing solutions for the Schrödinger Poisson system

<p style='text-indent:20px;'>We consider the following nonlinear Schrödinger-Poisson system</p><p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> $ \left\{\begin{array}{lll} -\Delta u+u+\lambda\phi(x) u = f(u)&\quad &x\in \mathbb{R}^3, \\ -\Delta \phi = u^2, \ \lim\limits_{|x|\to\infty} \phi(x) = 0&\quad &x\in \mathbb{R}^3, \end{array}\right. $ </tex-math></disp-formula></p><p style='text-indent:20px;'>where <inline-formula><tex-math id="M1">$ \lambda>0 $</tex-math></inline-formula> and <inline-formula><tex-math id="M2">$ f $</tex-math></inline-formula> is continuous. By combining delicate analysis and the method of invariant subsets of descending flow, we prove the existence and asymptotic behavior of infinitely many radial sign-changing solutions for odd <inline-formula><tex-math id="M3">$ f $</tex-math></inline-formula>. The nonlinearity covers the case of pure power-type nonlinearity <inline-formula><tex-math id="M4">$ f(u) = |u|^{p-2}u $</tex-math></inline-formula> with the less studied situation <inline-formula><tex-math id="M5">$ p\in(3, 4). $</tex-math></inline-formula> This result extends and complements the ones in [Z. Liu, Z. Q. Wang, and J. Zhang, Ann. Mat. Pura Appl., 2016] from the coercive potential case to the constant potential case.</p>

Partial Discharge Pattern Recognition Based on Synchrosqueezing Wavelet Transform and Multi-Scale Characteristic Parameters

Aiming at the high dimension of the characteristic of partial discharge and its high sensitivity to noise, firstly, the Synchrosqueezing wavelet transform is used to decompose the four typical partial discharge signals of transformers to overcome the defects of spectrum aliasing and energy leakage between real wavelet packet decomposition sub-bands.; Then, using the difference of energy and complexity of PD signals at different decomposition scales, the parameters of multi-scale energy and multi-scale energy spectrum entropy are used as the feature quantity of discharge type identification; Finally, the extracted features support vector machine classifier for discharge pattern recognition. Experimental results show, the proposed method can achieve better recognition than EMD and wavelet packet decomposition, and proves the effectiveness of the proposed method.

基于EMD分解的海浪有效波高短期预测研究

基于EMD分解的海浪有效波高短期预测研究

Identification of blasting vibration and coal-rock fracturing microseismic signals

Α new method based on variational mode decomposition (VMD) is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each mode component is extracted to form the eigenvector of the energy of the original signal and calculate the center of gravity coefficient of the energy distribution plane. Finally, the coal-rock fracturing and blasting vibration signals are classified using a decision tree stump. Experimental results suggest that VMD can effectively separate the signal components into coal-rock fracturing and blasting vibration signals based on frequency. The contrast in the energy distribution center coefficient after the dimension reduction of the energy distribution eigenvector accurately identifies the two types of microseismic signals. The method is verified by comparing it to EMD and wavelet packet decomposition.

Short-Term Power Load Forecasting Based on EMD-Grey Model

With the issuance of electricity reform No. 9 document in 2015, a new round of power system reform in China has been continuously pushed forward. With the gradual development of the pilot spot market in various provinces, the importance of load forecasting to the various main bodies of the spot power market has been constantly revealed. In order to improve the accuracy of short-term load forecasting in the spot market, and better highlight the randomness, periodicity and related trend of load fluctuation, this paper proposes a short-term load forecasting based on grey model and the EMD combination model, predict the future 24-hour load. In other words, GM(1,1) is used to predict the residual value sequence of EMD decomposition. In order to ensure the stability of the residual value sequence, improve the accuracy of the prediction and improve the effect of short-term load forecasting. Combined with MATLAB tools, the combined prediction model was simulated and verified by using the America PJM power market load data. The comparison results of the combined model with the single GM(1,1) and GM(1,2) respectively show that the combined model can significantly improve the accuracy of load forecasting compared with the traditional grey model method, providing the method guidance for load forecasting to better participate in the demand response under the new market environment.

Quasi-bivariate variational mode decomposition as a tool of scale analysis in wall-bounded turbulence

The identification and separation of multi-scale coherent structures is a critical task for the study of scale interaction in wall-bounded turbulence. Here, we propose a quasi-bivariate variational mode decomposition (QB-VMD) method to extract structures with various scales from instantaneous two-dimensional (2D) velocity field which has only one primary dimension. This method is developed from the one-dimensional VMD algorithm proposed by Dragomiretskiy and Zosso (IEEE Trans Signal Process 62:531–544, 2014) to cope with a quasi-2D scenario. It poses the feature of length-scale bandwidth constraint along the decomposed dimension, together with the central frequency re-balancing along the non-decomposed dimension. The feasibility of this method is tested on both a synthetic flow field and a turbulent boundary layer at moderate Reynolds number ( $$Re_{\tau }$$ = 3458) measured by 2D particle image velocimetry (PIV). Some other popular scale separation tools, including pseudo-bi-dimensional empirical mode decomposition (PB-EMD), bi-dimensional EMD (B-EMD) and proper orthogonal decomposition (POD), are also tested for comparison. Among all these methods, QB-VMD shows advantages in both scale characterization and energy recovery. More importantly, the mode mixing problem, which degrades the performance of EMD-based methods, is avoided or minimized in QB-VMD. Finally, QB-VMD analysis of the wall-parallel plane in the log layer (at $$y/\delta$$ = 0.12) of the studied turbulent boundary layer shows the coexistence of large- or very large-scale motions (LSMs or VLSMs) and inner-scaled structures, which can be fully decomposed in both physical and spectral domains.

Vacillations Analysis of Monthly Reference Crop Evapotranspiration Based on EMD Method

In this study, Fuxin city was taken as example to study Vacillations State features of reference crop evapotranspiration (ET0) in different time scale. Monthly ET0 was calculated based on four meteorological factors daily data of temperature (T), wind speed (WS), relative humidity (RH) and sunshine number (SN) during 1954 to 2006 in the two weather stations of Fuxin and Zhangwu located in Fuxin city using Penman-Monteith formula. EMD method was conducted to analyze Vacillations characteristics. Results showed that: Decreases in oscillation frequency and amplitude was observed, while cycle increased in turn after EMD decomposition of ET0 and four meteorological factors gradually. The rule of IMF changes transformed to liner from non-linear. Maximum contribution rates of T, WS, SN, RH, and ET0, were IMF1 (93.0%), IMF2 (35.0%), IMF2 (64.5%), IMF2 (40.0%) and IMF2 (84.3%). The main oscillation cycles were 1, 0.6, 0.8, 0.7, and 1 year, respectively. Study on ET0 oscillation mode based on EMD method, provided reference for ET0 and a new resolving tool for ET0 study.

Electrical Signal Analysis for Bearing Fault of Wind Turbine Based on EMD

In this paper, according to the bearing current signal is nonstationary and nonlinear characteristics, establish of doubly fed wind turbine simulation model and test bench, respectively under the normal and fault condition of bearing of motor current signal by EMD processing and analysis. Through the simulation analysis and experimental verification, it shows that after the EMD decomposition of the IMF component can effectively isolate the fault information in the current signal.EMD is an effective method of current signal processing

Application of the Empirical Mode Decomposition method for the prediction of the tool wear in turning operation

Tool wear is one of the factors to consider since its evolution damages and degrades the surface roughness of machined material. For this raison, this article proposes the application of a new time-frequency method, called Empirical Mode Decomposition, for the prediction of the tool wear in turning operation. The proposed method is applied on vibratory acceleration signals measured during the cutting process in different configurations. The sifting process of the EMD method decomposes the measured signal into several Intrinsic Modes Functions allowing its time-frequency analysis. A time and frequency domain analyses have been carried out. In time domain, the tool wear monitoring is performed by using two scalar indicators; the energy and the mean power of the first IMF of the EMD decomposition. The variation of these indicators over the entire tool life highlights the three periods and allows locating the transition point between the wear stabilization and the wear acceleration period. The prediction of the tool ageing is then very clear avoiding its collapse and the machining fail. In the frequency domain a new indicator, based on the amplitude of the peak corresponding to the tool’s natural frequency, is proposed. It has been shown that the variation of this indicator over the entire tool life is the same as for the scalar indicators. The three periods are shown and the critical transition point reflecting the beginning of the tool ageing is clearly detectable. The experimental results are very promising in industrial environment for the implementation of online monitoring system for cutting tool state. DOI: http://dx.doi.org/10.5755/j01.mech.23.2.8971

How does economic policy uncertainty interact with sovereign bond yield? Evidence from the US

This paper proposed a multi-scale correlations perspective to explore the relationship between economic policy uncertainty and sovereign bond yield in the United States, using the Ensemble EMD decomposition algorithm and Pearson correlation coefficient. Results show that these two variables exhibit a completely opposite linear relationship in the long-term trend before and after 27 April 2009, indicating that there is obvious conversion of stage characteristics and the relationship between these two variables is complex. Therefore, further study to investigate this complex relationship using nonlinear correlation measurement is necessary, and all these research will contribute to the policy making of regulators and trading behaviors of investors in the sovereign bond market.

Empirical Mode Decomposition and Quantization Effects

The article deals with phenomena that arise when trying to apply EMD decomposition of signals with quantization noise. It explains the basic procedures of EMD as a part of Hilbert-Huang transform and shows how it can be affected by quantization. A simple method to suppress these phenomena is proposed and examples to illustrate the functionality of this method are shown.

The Tourism Demand of Nonlinear Combination Forecasting based on Time Series Method and WNN

The combination forecasting model IOWGA-EMD-ARMA-WNN is proposed in this paper. The randomness, periodicity and tendency of the original data are showed by EMD decomposition in EMD-ARMA model. WNN combines the advantages of wavelet analysis and BP neural network and improves the learning efficiency and forecasting accuracy. The weight of combination model is decided by forecasting precision of EMD-ARMA model and WNN model based on IOWGA method. At last, the IOWGA-EMD-ARMA-WNN model is used to forecast monthly inboard tourism demand of China and the results show that the proposed combination model has better performance on forecasting accuracy compared with the other models.

澜沧江流域极端天气灾害特征及波动趋势研究

The meteorological data from1961 to 2010 about the Lancang region which was provided by National Meteorological Information Center were used to to explore the link between the variation of the Lancang River Basin extreme weather disasters in the late 19 th century to the early 21 st century,and its regional extreme disasters change and global climate change the method whose using wavelet analysis,EMD decomposition,CI index,mean generating function stepwise regression model,mutation testing and correlation analysis etc were been used. It is showed that by the results:( 1) Annual precipitation is stabilized,rose slightly,but the rate of rise is small,only 3.1848 in the 50-year period of which from 1951 to 2010. Annual precipitation anomaly maps to reflect regional differences in precipitation in the south than in the north,between positive and negative anomalies between-2 to 2,the probability of occurrence of the northern arid.( 2) In the past20 years,the Lancang area arid the number was significantly increased by the Lancang River Basin annual rainstorm frequency times in the past 50 years and the next 20 years there is no obvious increase trend,drought frequency times the next 20 years showed a slope of 0. 2635 the rising trend,the future of the region of extreme weather disasters,mainlydrought disaster.( 3) There have multi-scale features about regional precipitation and storm frequency,whose 2a,7a,15 a change cycle in Lancang River Basin precipitation has been proved by two research methods,but whose main cycle by two method is different. It is showed that EMD method is more suitable than wavelet method to deal with non-stationary,nonlinear signal,concluded that the precipitation of the Lancang River Basin 2a,7a,15 a change cycle,and primary cycle prevail 2a.( 4) The cycle of IMF1 and IMF2 about precipitation and frequent rainstorms the degree sequence is between2—7 a,which is coincided similar to that of ENSO. It was inferred by it that there is important connection between the storms and drought disasters of Lancang River Basin and ENSO. As the temperature increased significantly increased frequency of drought disasters,all of those showed that extreme temperature disasters,climate change and global warming have some connection to one of the manifestations of global climate change,regional response.

The Time-Frequency Analysis Method Based on EMD White Noise Energy Density Distribution Characteristics of the Internal Combustion Engine Vibration

For the limitations of HHT of the internal combustion engine vibration signal analysis, and the problem of WVD cross-term suppression methods existing aggregation and cross-term component suppression conflicting, the time-frequency analysis method based on EMD white noise energy density distribution characteristics of the internal combustion engine vibration is proposed. First, the internal combustion engine vibration signal was decomposed into the independent series intrinsic mode function (IMF) with different characteristic time scales by using EMD decomposition method. Then, based on the energy density distribution characteristics of the white noise in EMD decomposition, used the distribution interval estimation curve of the IMFs energy density logarithm of white noise with the same length of the original signal as cordon for false pattern component, identified and eliminated false mode component of vibration signal IMFs component, analysised of each IMF with Wigner-Ville. Finally, the Wigner-Ville analysis results of each IMF were linear superposed in order to reconstruct the original signal time-frequency distribution. Simulation and engine vibration time-frequency analysis results show that this method has an excellent time-frequency characteristics, and can successfully extract feature information of the internal combustion engine cylinder head vibration signal.

Improvement Study of EEMD Decomposition Efficiency Based on CUDA Architecture

Abstract EEMD can inhibit mode mixing, which may occur in EMD, EEMD is a technology of adding many groups of white noise to original signal to do assisted analysis on the basis of EMD, however, it will greatly reduce the decomposition efficiency of the signal. In order to eliminate the effects of mode mixing, and improve decomposition efficiency of EEMD, this paper proposes a kind of EEMD based on GPU+CPU, the core idea of algorithm is to implement EMD decomposition’s iterative process control procedure by CPU, and the parts involved computing tasks are implemented by GPU. Decompose simulated signals and EEG respectively by using EEMD before and after improvement, and compares decomposition performance, the results show that, improved algorithm can greatly improve decomposition efficiency of EEMD.

Mechanical Fault Diagnosis Based on LMD-Approximate Entropy and LSSVM

Mechanical fault diagnosis is a feature extraction and pattern recognition process. The feature extraction is related to the accuracy of fault diagnosis and the reliability of the prediction. How to extract fault features effectively and build an accurate model to recognize fault is the key of diagnosis technology. Accordingly, a feature extraction method based on LMD-approximate entropy was proposed, and combined it with LSSVM to diagnose mechanical fault. Firstly, the decomposition of fault feature by LMD, and then the approximate entropy of product function were taken to extract fault features accurately. Finally, the eigenvectors were input to LS-SVM for fault recognition. Compared with EMD and wavelet decomposition, the results show that it can extract fault features effectively and can improve the accuracy and speed of fault diagnosis. DOI: http://dx.doi.org/10.11591/telkomnika.v11i2.2028

Development of Empirical Mode Decomposition

In this paper, one of the tasks for which empirical mode decomposition is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thresholding, a similar technique adapted to EMD is developed, leading to enhanced denoising performance.

Noise Reduction of the Transient Electromagnetic Weak Signal under Strong Noise Based on Power Detection of EMD

The method of Transient Electromagnetic(TEM) detection is widely applied to the field of coal mine and oil exploration. But since Transient Electromagnetic signal is a non-stationary , nonlinear , weak signal under the background of heavy noise , traditional method which is based on the signal superposition is only suited to the signal of the superficial layer , it can not fetch the weak signal of the deep layer. noise reduction of the transient electromagnetic weak signal under heavy noise based on power detection of EMD decomposition is presented in this paper. This method does not need prior information. Through the method, the transient electromagnetic weak signal under heavy noise is decomposed to several intrinsic mode functions. Then by power detection, the noise leading component is extracted and reduced .At last the transient electromagnetic weak signal is obtained adaptively. The example of this paper show: when the received TEM signal of deep layer is -30db SNR, This method can reduce the most of noise, obtain the transient electromagnetic weak signal which contains geological information, and improve the depth and accuracy of the TEM detection.

Boundary-processing-technique in EMD method and Hilbert transform

By virtue of neural network, a series of signals is extended forward and backward, as a result, two additional maxima and two additional minima are obtained at both ends of the original data set, with which the EMD decomposition can be exactly achieved with cubic spline interpolation. Meanwhile, by using of neural network every IMF component can also be extended forward and backward, which effectively restrains the end effect, thus the veracious Hilbert spectra are achieved. Verifications of the sample signals and the actual surface elevation of sea waves show that the present extension method is relatively accurate.