NSHV trajectory prediction algorithm based on aerodynamic acceleration EMD decomposition

Aiming at the problem of gliding near space hypersonic vehicle(NSHV)trajectory prediction,a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition(EMD)is proposed.The method analyzes the motion characteristics of the skipping gliding NSHV and verifies that the aerodynamic acceleration of the target has a relatively stable rule.On this basis,EMD is used to extract the trend of aerodynamic acceleration into multiple sub-items,and aggregate sub-items with similar attributes.Then,a prior basis function is set according to the aerodynamic acceleration stability rule,and the aggregated data are fitted by the basis function to predict its future state.After that,the prediction data of the aerodynamic acceleration are used to drive the system to predict the target trajectory.Finally,experiments verify the effectiveness of the method.In addition,the distribution of prediction errors in space is discussed,and the reasons are analyzed.

基于差分处理的EMD-LSTM短时空中交通流量预测

随着中国民航的飞速发展,终端区空中交通流量与日俱增,短时空中交通流量预测对于精准实施空中交通流量管理具有重要意义。为提高短时空中交通流量预测的准确性,提出了基于数据差分处理(data differential processing)的经验模态分解(empirical mode decomposition,EMD)和长短期记忆(long short-term memory,LSTM)相结合的短时空中交通流量预测模型。首先,该模型对短时空中交通流量序列进行经验模态分解;其次,为了提高预测精度,运用数据差分对时间序列进行平稳化处理;最后,将平稳处理后的序列分别输入LSTM网络模型进行预测,经过数据重构,得到最终的短时流量预测值。利用郑州新郑国际机场数据进行了实验验证,结果表明,该模型预测精度和拟合程度的典型指标RSME、MAE、R^(2)分别为0.29%,0.08%、96.40%,相较于其他方法,预测精度大幅度提高,可以为短时空中交通流量预测提供有益参考。

基于EMD的振动信号去噪方法研究

煤矿机械在重载情况下运行,其振动信号往往具有非线性、不平稳等特性,其不仅带有大量设备运动状态的信息,同时也夹杂着大量的环境噪声,无法直接对其进行分析。而经验模式分解(EMD)在处理非线性、非平稳信号时具有一定优势,是一种自适应的信号处理方法。针对煤矿机械振动信号的特性,提出基于EMD的去噪方法,首先将振动信号进行EMD分解,得到各固有模态函数(IMF),然后计算各IMF与原始信号的相关系数,并将相关系统按照从小到大进行排序,通过相邻两个相关系数的差值最大,找到敏感IMF分量重构,实现非平稳信号的滤波,为机械设备后期故障诊断奠定了良好基础。并通过实验数据分析,验证了EMD方法对振动信号进行去噪的有效性及可行性。

基于EMD近似熵和LS-SVM的机械故障智能诊断

故障特征提取的精确性和分类识别的高效率是提高故障诊断准确率和速度的关键,针对此问题,提出一种基于经验模式分解(empirical mode decomposition,EMD)近似熵和最小二乘支持向量机(least square support vector machine,LS-SVM)的机械故障诊断新方法。利用EMD良好的局域化特性和近似熵表征信号复杂性规律来量化故障特征,再与LS-SVM相结合进行故障类型识别。首先,对故障振动信号进行EMD分解,得到若干个反映故障信息的本征模函数(intrinsic mode function,IMF);其次,选取前4个IMF的近似熵值作为信号的特征向量;最后将构造的特征向量输入到LS-SVM分类器进行故障类型识别。仿真表明,该方法能有效地提取故障特征,与传统的BP(back propagation)网络相比,具有训练样本少、训练时间短、识别率高等优点。

APPLICATION OF IMPROVED EMD IN VIBRATION SIGNAL FEATURE EXTRACTION OF VEHICLE

In order to truly obtain the feature extraction of vibration signals under the strong background noise, the analysis and improvement of empirical mode decomposition （EMD） is carried on. After that, the improved EMD is applied to the feature extraction of vehicle vibration signals. First, the multi-autocorrelation method is adopted in each input signal,so the noise is reduced effectively. Then, EMD is used to deal with these signals,and the intrinsic mode functions （IMFs） are obtained. Finally, for obtaining the feature information of these signals, the Hilbert transformation and the spectrum analysis are performed in some IMFs. Theoretical analysis and ex- periment verify the effectiveness of the method, which are valuable reference for the same engineering problems.

融合加速度信息的动态心电EMD滤波算法

在远程医疗和家庭健康诊断中,医护手环在进行心电信号采集时,因被测试者呼吸和抖动的影响,心电信号中会夹杂运动伪迹噪声。为了有效滤除心电信号中的运动伪迹,将加速度信息加入到EMD心电滤波算法当中,通过对被测者运动状态的判断和分类,选用合适的阈值和滤波算法分解项对心电信号进行处理。通过自制手环进行心电采集,使用该算法进行处理,达到较好的滤波效果。

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.

Texture classification based on EMD and FFT

Empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process that reflects human’s visual mechanism of differentiating textures. In this paper, we present a modified 2D EMD algorithm using the FastRBF and an appropriate number of iterations in the shifting process (SP), then apply it to texture classification. Rotation-invariant texture feature vectors are extracted using auto-registration and circular regions of magnitude spectra of 2D fast Fourier transform (FFT). In the experiments, we employ a Bayesion classifier to classify a set of 15 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing datasets for images with different orientations, show the effectiveness of the proposed classification scheme.

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.

Identification of acceleration pulses in near-fault ground motion using the EMD method

In this paper, response spectral characteristics of one-, two-, and three-lobe sinusoidal acceleration pulses are investigated, and some of their basic properties are derived. Furthermore, the empirical mode decomposition （EMD） method is utilized as an adaptive filter to decompose the near-fault pulse-like ground motions, which were recorded during the September 20, 1999, Chi-Chi earthquake. These ground motions contain distinct velocity pulses, and were decomposed into high-frequency （HF） and low-frequency （LF） components, from which the corresponding HF acceleration pulse （if existing） and LF acceleration pulse could be easily identified and detected. Finally, the identified acceleration pulses are modeled by simplified sinusoidal approximations, whose dynamic behaviors are compared to those of the original acceleration pulses as well as to those of the original HF and LF acceleration components in the context of elastic response spectra. It was demonstrated that it is just the acceleration pulses contained in the near-fault pulse-like ground motion that fundamentally dominate the special impulsive dynamic behaviors of such motion in an engineering sense. The motion thus has a greater potential to cause severe damage than the far-field ground motions, i.e. they impose high base shear demands on engineering structures as well as placing very high deformation demands on long-period structures.

Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification

This paper presents an effective and efficient combination of feature extraction and multi-class classifier for motion classification by analyzing the surface electromyografic(sEMG) signals. In contrast to the existing methods,considering the non-stationary and nonlinear characteristics of EMG signals,to get the more separable feature set,we introduce the empirical mode decomposition(EMD) to decompose the original EMG signals into several intrinsic mode functions(IMFs) and then compute the coefficients of autoregressive models of each IMF to form the feature set. Based on the least squares support vector machines(LS-SVMs) ,the multi-class classifier is designed and constructed to classify various motions. The results of contrastive experiments showed that the accuracy of motion recognition is improved with the described classification scheme. Furthermore,compared with other classifiers using different features,the excellent performance indicated the potential of the SVM techniques embedding the EMD-AR kernel in motion classification.

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.

Dynamic unbalance detection of cardan shaft in high-speed train based on EMD-SVD-NHT

Contrary to the aliasing defect between the adjacent intrinsic model functions(IMFs) existing in empirical model decomposition(EMD), a new method of detecting dynamic unbalance with cardan shaft in high-speed train was proposed by applying the combination between EMD, Hankel matrix, singular value decomposition(SVD) and normalized Hilbert transform(NHT). The vibration signals of gimbal installed base were decomposed through EMD to get different IMFs. The Hankel matrix constructed through the single IMF was orthogonally executed through SVD. The critical singular values were selected to reconstruct vibration signs on the basis of the key stack of singular values. Instantaneous frequencys(IFs) of reconstructed vibration signs were applied to detect dynamic unbalance with shaft and eliminated clutter spectrum caused by the aliasing defect between the adjacent IMFs, which highlighted the failure characteristics. The method was verified by test data in the unbalance condition of dynamic cardan shaft. The results show that the method effectively detects the fault vibration characteristics caused by cardan shaft dynamic unbalance and extracts the nature vibration features. With comparison to the traditional EMD-NHT, clarity and failure characterization force are significantly improved.

Separation of closely spaced modes by combining complex envelope displacement analysis with method of generating intrinsic mode functions through filtering algorithm based on wavelet packet decomposition

One of the important issues in the system identification and the spectrum analysis is the frequency resolution, i.e., the capability of distinguishing between two or more closely spaced frequency components. In the modal identification by the empirical mode decomposition （EMD） method, because of the separating capability of the method, it is still a challenge to consistently and reliably identify the parameters of structures of which modes are not well separated. A new method is introduced to generate the intrin- sic mode functions （IMFs） through the filtering algorithm based on the wavelet packet decomposition （GIFWPD）. In this paper, it is demonstrated that the CIFWPD method alone has a good capability of separating close modes, even under the severe condition beyond the critical frequency ratio limit which makes it impossible to separate two closely spaced harmonics by the EMD method. However, the GIFWPD-only based method is impelled to use a very fine sampling frequency with consequent prohibitive computational costs. Therefore, in order to decrease the computational load by reducing the amount of samples and improve the effectiveness of separation by increasing the frequency ratio, the present paper uses a combination of the complex envelope displacement analysis （CEDA） and the GIFWPD method. For the validation, two examples from the previous works are taken to show the results obtained by the GIFWPD-only based method and by combining the CEDA with the GIFWPD method.

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.

A method for constraining the end effect of EMD based on sequential similarity detection and adaptive filter

Aimed at the problem of the end effect when using empirical mode decomposition(EMD),a method for constraining the end effect of EMD is proposed based on sequential similarity detection and adaptive filter.The method divides the signal into many wavelets,and it changes the initial wavelet length to select the best initial wavelet that has the minimum error and maximum number of matching seed wavelets,and the wavelet slopes are used for pre-matching and secondary matching to speed up the matching speed.Then,folded self-adaptive threshold is used to select multiple seed wavelets,and finally the end waveform is predicted and expanded according to the adaptive filter method.The proposed method is used to analyze the non-stationary nonlinear simulation signal and experimental signal,and it is compared with the mirror extension and RBF extension methods.The orthogonality index and similarity index of the EMD results of the extended signal after the proposed method are better than those of the other methods.The results show that the proposed method can better constrain the end effect,and has certain validity,accuracy and stability in solving the end effect problem.

A New Multi-sensor Data Fusion Algorithm Based on EMD-MMSE

A new multi-sensor data fusion algorithm based on EMD-MMSE was proposed.Empirical mode decomposition(EMD)is used to extract the noise of every time series for estimating the variance of the noise.Then minimum mean square error(MMSE)estimator is used to calculate the weights of the corresponding series.Finally,the fused signal is the weighted addition of all these series.The experiments in lab testified the efficiency of this method.In addition,the comparison in fusion time and fusion results with existing fusion method based on wavelet and average technique shows the advantage of this method greatly.

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.

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.

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.