Determination of Instantaneous Frequencies of Low Plasma Waves in the Magnetosheath Using Empirical Mode Decomposition (EMD) and Hilbert Transform (HT)

The observations of in-situ spacecraft mission in the magnetosheath and a region of thermalized subsonic plasma behind the bow shock reveal a non-linear behaviour of plasma waves. The study of waves and optics in Physics has given the understanding of the effect of many waves coming together to form a wave field or wave packet. The common aspect of such study shows that two or more waves can superimpose constructively or destructively. The sudden high magnetic field data in the magnetosheath displays such possibility of superposition of waves. In this paper, we use the empirical mode decomposition (EMD) and Hilbert transform (HT) techniques to determine the instantaneous frequencies of low frequency plasma waves in the magnetosheath. Our analysis has shown that the turbulent behavior of magnetic field in the magnetosheath within the selected period is due to superposition of waves.

Satellite fault diagnosis method based on predictive filter and empirical mode decomposition

A novel satellite fault diagnosis scheme is presented based on the predictive filter and empirical mode composition（EMD）.First,the predictive filter is utilized to obtain the fault estimation,which is corrupted by noise.Then the EMD method is introduced to decompose the fault estimation into a finite number of intrinsic mode functions and extract the trend of faults for fault diagnosis.The proposed scheme has the ability of diagnosing both abrupt and incipient faults of the actuator in a satellite attitude control subsystem.A mathematical simulation is given to illustrate the effectiveness of the proposed scheme.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

FAULT DIAGNOSIS APPROACH FOR ROLLER BEARINGS BASED ON EMPIRICAL MODE DECOMPOSITION METHOD AND HILBERT TRANSFORM

Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.

HARMONIC COMPONENT EXTRACTION FROM A CHAOTIC SIGNAL BASED ON EMPIRICAL MODE DECOMPOSITION METHOD

A novel approach of signal extraction of a harmonic component fRom a chaotic signal generated by a Duffing oscillator was proposed. Based on empirical mode decomposition （EMD） and concept that any signal is composed of a series of the simple intrinsic modes, the harmonic components were extracted f^om the chaotic signals. Simulation results show the approach is satisfactory.

Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor

On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.

Signal prediction based on empirical mode decomposition and artificial neural networks

In view of the usefulness of Empirical Mode Decomposition （EMD）, Artificial Neural Networks （ ANN）, and Most Relevant Matching Extension （MRME） methods in dealing with nonlinear signals, we pro- pose a new way of combining these methods to deal with signal prediction. We found the results of combining EMD with either ANN or MRME to have higher prediction precision for a time series than the result of using EMD alone.

NON-DESTRUCTIVE PAVEMENT LAYER THICKNESS MEASUREMENT USING EMPIRICAL MODE DECOMPOSITION WITH GPR

Ground Penetrating Radar(GPR) is an effective Non-Destructive Testing(NDT) technique for highway pavement surveys, which is able to acquire continuous pavement data compared with traditional core drilling method. In this study, we proposed an accurate and efficient method to estimate the thickness of each pavement layer using an air-coupled GPR system. For this work, the main difficulties are estimating each pavement layer's time delay and dielectric constant. We first give the basic signal model for pavement evaluation, and then present an Intrinsic Mode Functions(IMFs) product detector to determine each pavement layer's time delay. This method is based on Empirical Mode Decomposition(EMD), which is an adaptive signal decomposition procedure and proved to be suitable for suppressing noises in GPR signal. The dielectric constant was determined by metal reflection measurement. The laboratory and highway experiments illustrate that the proposed thickness estimation method yields reasonable result, thus meets the requirements of practical highway pavement survey with massive GPR data.

Improvement of the prediction accuracy of polar motion using empirical mode decomposition

Previous studies revealed that the error of pole coordinate prediction will significantly increase for a prediction period longer than 100 days, and this is mainly caused by short period oscillations. Empirical mode decomposition （EMD）, which is increasingly popular and has advantages over classical wavelet decomposition, can be used to remove short period variations from observed time series of pole co- ordinates. A hybrid model combing EMD and extreme learning machine （ELM）, where high frequency signals are removed and processed time series is then modeled and predicted, is summarized in this paper. The prediction performance of the hybrid model is compared with that of the ELM-only method created from original time series. The results show that the proposed hybrid model outperforms the pure ELM method for both short-term and long-term prediction of pole coordinates. The improvement of prediction accuracy up to 360 days in the future is found to be 24.91% and 26.79% on average in terms of mean absolute error （MAE） for the xp and yp components of pole coordinates, respectively.

Segmented second algorithm of empirical mode decomposition

A new algorithm, named segmented second empirical mode decomposition （EMD） algorithm, is proposed in this paper in order to reduce the computing time of EMD and make EMD algorithm available to online time-frequency analysis. The original data is divided into some segments with the same length. Each segment data is processed based on the principle of the first-level EMD decomposition. The algorithm is compared with the traditional EMD and results show that it is more useful and effective for analyzing nonlinear and non-stationary signals.

Weighing axle weight of moving vehicle based on empirical mode decomposition

Dynanfic forces are the main factor that influences the axle weight measurement accuracy of moving vehicle. Empirical mode decomposition （EMD） is presented to separate the dynamic forces contained in the axle weight signal. The concept and algorithm of EMD are introduced. The characteristic of the axle weight signal is analyzed. The method of judging pseudo intrinsic mode function （pseudo-IMF） is presented to improve the weighing accuracy. Numerical simulation and field experiments are conducted to evaluate the performance of EMD. The result shows effectiveness of the proposed method. Maximum weighing errors of the front axle, the rear axle and the gross weight at the speed of 15 km/h or lower are 2.22%, 6.26% and 4.11% respectively.

Sensitivity of intrinsic mode functions of Lorenz system to initial values based on EMD method

Extreme sensitivity to initial values is an intrinsic character of chaotic systems. The evolution of a chaotic system has a spatiotemporal structure containing quasi-periodic changes of different spatiotemporal scales. This paper uses an empirical mode decomposition （EMD） method to decompose and compare the evolution of the time-dependent evolutions of the x-component of the Lorenz system. The results indicate that the sensitivity of intrinsic mode function （IMF） component is dependent on initial values, which provides some scientific evidence for the possibility of long-range climatic prediction.

Mitigating end effects of EMD using non-equidistance grey model

Aiming at mitigating end effects of empirical mode decomposition （EMD）, a new approach motivated by the non- equidistance grey model （NGM） termed as NGM（1,1） is proposed. Other than trapezoid formulas, the cubic Hermite spline is put forward to improve the accuracy of derivative to the accumulated generating operation （AGO） series. Hopefully, it is worth stressing that the proposed NGM（1,1） model is particularly useful for predicting uncertainty data. Qualitative and quantitative comparisons between the proposed approach and other well-known algorithms are carried out through computer simulations on synthetic as well as natural signals. Simulation results demonstrate the proposed method can reduce end effects and improve the decomposition results of EMD.

基于EMD-DESN的无人机集群航迹目的地预测

无人机(unmanned aerial vehicle,UAV)集群作战样式多样、运动模式复杂,导致集群航迹目的地难以预测。为解决上述问题,本文提出了一种基于经验模态分解(empirical mode decomposition,EMD)和深度回声状态网络(deep echo state network,DESN)的UAV集群航迹目的地预测算法。为使集群运动模型更真实地模拟UAV集群作战过程,本文引入航向误差时变方差,改进了Olfati-Saber集群运动模型的虚拟领导项。为处理因群内的协同作用和集群航向误差导致的运动非平稳性,引入了EMD,对UAV航迹序列进行重构。考虑到获知航迹的时序性,设计了滑窗结构,采用DESN对重构航迹的不同时段进行目的地预测。仿真实验结果表明,本文提出的EMD-DESN算法较基本DESN算法能以更高的准确度预测UAV集群航迹目的地,并能更早地实现稳定的正确预测。

基于改进EMD-CIIT降噪算法的INS/5G组合导航方法

以惯性导航系统(inertial navigation system,INS)/5G组合导航系统为研究对象,首先,针对低成本的惯性传感器信噪比(signal to noise ratio,SNR)较低进而影响组合导航精度的问题,提出一种改进阈值的清除迭代经验模态分解间隔阈值(clear iterative empirical mode decomposition interval-thresholding,EMD-CIIT)算法,有效提升惯性传感器的SNR,以及提升组合导航系统的定位精度。然后,针对同频5G机会信号的同频干扰、钟差、钟漂等因素导致伪距值异常的问题,提出一种基于自适应卡尔曼滤波的紧组合导航算法,利用基于马氏距离的5G伪距置信度方案,实时调整观测协方差矩阵,从而抑制伪距异常值对定位精度的影响,进一步提高定位的可靠性。最后,分别采用数值仿真与实验手段验证所提方案的有效性和优越性。

基于CEEMDAN和改进的混合时间序列模型工作面涌水量预测研究

为提高采煤工作面涌水量预测准确度,收集大量工作面涌水量观测数据进行整理、统计、分析,将涌水量稳定性、周期性和季节性特征考虑在内,提出1种基于数据驱动的完全自适应模态分解算法(CEEMDAN)和改进的混合时间序列模型工作面涌水量预测方法。该方法利用CEEMDAN处理涌水量数据,构建麻雀搜索算法(SSA)优化的长短期记忆网络(LSTM)和自回归移动平均模型(ARIMA)并行级联而成的混合时间序列模型对工作面涌水量进行预测。研究结果表明:该模型预测结果与真实数据相差更小,平均绝对误差为6.36 m 3/h,均方根误差为10.6 m 3/h,模型拟合系数为0.95,更适用于工作面涌水量预测。研究结果可为矿井工作面涌水量预测及防控提供参考。

基于EMD的地震数据速度谱优化方法

地震波在地层中的传播速度可间接反映地下岩性及地质构造特征,速度的提取与分析影响地震数据处理和解释全过程。目前,速度谱分辨率低,导致拾取的速度不准确,构建的速度模型精度经常不能满足复杂地质构造的地震成像要求。为此,提出基于经验模态分解(EMD)的地震数据速度谱优化方法。该方法是一种频移处理技术,能有效提高地震数据低频端能量的信噪比。首先,基于Hilbert变换获得地震数据的瞬时振幅;其次,对瞬时振幅进行EMD;然后,筛选分解后的本征模量(IMF),选择具有有益表达速度谱信息的本征模态模量;最后,构建新的速度谱数据。经过优化后的地震数据频谱分辨率更高,有效频带向低频端移动。实验测试和实际资料处理结果表明,所提方法能有效扩大速度谱拾取的寻优区间,提高速度分析准确性,提升地震资料成像品质。该方法在成果数据处理和速度谱优化方面具有广泛的应用价值。

基于改进EMD方法与11/2谱的DEMON谱提取方法

噪声的包络调制检测(Detection of Envelope Modulation on Noise,DEMON)谱分析技术已被广泛应用于特征提取领域,但经典DEMON谱提取中高频信号频段的选取会影响DEMON谱的提取效果。针对这一问题,文中首先运用经验模态分解(Empirical Mode Decomposition,EMD)方法获得一系列固有模态函数(Intrinsic Mode Function,IMF),依据各阶模态函数与原信号的相关程度,筛选出更具代表性的几阶固有模态函数进行解调,再对解调的结果运用11/2维谱分析方法进行谱分析以抑制高斯噪声,通过这种方法获得的DEMON谱信噪比优于传统方法。实测湖试数据分析结果表明,该改进方法可以有效地进行特征提取,结果优于经典DEMON谱分析方法;该改进方法具有一定的实用性,有利于进行后续目标分类识别。

强噪声条件下基于EMD-AE优选特征的离心泵多故障诊断方法

工业离心泵故障诊断中常常受到噪声的干扰,针对这一问题,提出一种强噪声条件下基于经验模态分解(empirical mode decomposition,EMD)和自编码器的优选特征方法。首先利用补偿距离评估技术确定出有效的时频特征,然后通过EMD处理,得到包含不同尺度和频率特性的模态分量。通过能量比变异系数确定出有效的分析分量,通过提取出所选分量的有效特征,拼接构造高维的深度特征。最后通过自编码器对深度特征做降维处理,进一步优选特征,得到最终的故障敏感特征,完成特征提取。选用支持向量机作为故障诊断模型,通过工业离心泵多故障数据进行对比试验。结果表明所提方法在信噪比为-5 dB、-7 dB和-10 dB强噪声干扰条件下,准确率较传统时频特征分别提高了6.13%、7.46%、12.00%。该方法有较强的抗噪声的能力,在噪声干扰下能有效提取表征设备状态的敏感特征。

基于EMD-DELM-LSTM组合模型的湖泊水位多时间尺度预测

针对水位时间序列具有线性与非线性混合、不确定性高等特点带来的预测困难问题,提出了一种基于经验模态分解(EMD)、长短时记忆网络(LSTM)和深度极限学习机(DELM)的EMD-DELM-LSTM组合模型,其中DELM和LSTM采用并联结构预测,并与EMD串联连接。首先使用EMD将原始信号分解为若干个具有单一特征的本征模态函数(IMFs),再将IMFs分类重组为高、中、低频信号后输入DELM-LSTM并联结构中进行预测并重构。以广州某大学重要湖泊为例验证模型的有效性,结果表明,与EMD-LSTM、EMD-DELM、LSTM、DELM和BiLSTM模型相比,本模型在不同时间尺度下的预测性能均有显著提升,其中40 min时间尺度下的预测性能提升效果最为明显,分别较对比模型提升43.08%、22.92%、45.79%、30.92%和47.31%。可见,本模型对于不同时间尺度的水位预测具有良好的可靠性和稳定性。