Application of empirical mode decomposition based energy ratio to vortex flowmeter state diagnosis

To improve the measurement performance, a method for diagnosing the state of vortex flowmeter under various flow conditions was presented. The raw sensor signal of the vortex flowmeter was adaptively decomposed into intrinsic mode functions using the empirical mode decomposition approach. Based on the empirical mode decomposition results, the energy of each intrinsic mode function was extracted, and the vortex energy ratio was proposed to analyze how the perturbation in the flow affected the measurement performance of the vortex flowmeter. The relationship between the vortex energy ratio of the signal and the flow condition was established. The results show that the vortex energy ratio is sensitive to the flow condition and ideal for the characterization of the vortex flowmeter signal. Moreover, the vortex energy ratio under normal flow condition is greater than 80%, which can be adopted as an indicator to diagnose the state of a vortex flowmeter.

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.

基于小波包分解与CEEMDAN能量熵的水电机组振动信号特征提取

针对水电机组振动信号非平稳、非线性及噪声问题,提出一种基于自适应噪声完备经验模态分解(CEEMDAN)与能量熵结合的特征提取方法,首先对采集的振动信号进行小波包降噪处理,然后对降噪后信号进行CEEMDAN分解,运用相关系数法筛选有效固有模态函数(IMF)并计算其能量熵,由此构建特征向量集,最后将其输入到海洋捕食者优化支持向量机算法(MPA-SVM)进行模式识别。基于模拟信号、实测信号验证所提特征提取方法的有效性,并与其他方法作对比。结果表明,基于小波包分解与CEEMDAN能量熵的特征提取方法能准确提取特征,有效区分机组不同状态,为工程领域提供了应用价值。

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。

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.

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。

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.

基于EMD分量与小波包能量熵的轧辊磨削颤振在线预测

针对轧辊磨削颤振时的时频域单一处理方法存在部分特征丢失的问题,提出了时频域相结合的方法对信号进行特征处理,并利用智能算法实现轧辊磨削颤振的在线预测。首先,利用经验模态分解(empirical mode decomposition,EMD)方法对振动传感器信号进行分解获得各固有模态函数(intrinsic mode function,IMF),剔除“虚假分量”后计算表征轧辊磨削颤振的时域特征。然后,利用小波包能量熵对声发射传感器信号求解频率段节点能量熵值,获得表征轧辊磨削颤振的频域特征。最后,将上述时频域特征降维后代入智能算法模型实现对轧辊磨削加工的在线预测。结果表明:LV-SVM模型的磨削颤振分类平均准确率达92.75%,模型平均响应时间为0.7765 s;验证了时频域特性的EMD和小波包能量熵方法的LV-SVM在线预测轧辊磨削颤振的有效性。

采用TVFEMD和瞬时能量比的轧辊磨床颤振在线监测方法

在磨削过程中,颤振是轧辊表面产生振纹的一个最重要的原因,严重影响了工件表面质量。为避免颤振带来的不利影响,提出了一种基于时变滤波经验模态分解(TVFEMD)和瞬时能量比的(IER)的颤振在线监测方法。该方法采用可靠的指标提前监测出颤振的发生,解决了轧辊磨床早期颤振特征微弱,在背景噪声下难以快速识别的问题。首先对实时采集的振动信号进行实时分段处理。其次对每个砂轮转动周期内的信号进行时变滤波经验模态分解,提高信噪比。然后运用瞬时频率和瞬时能量比选取颤振敏感频带,将颤振敏感频带的瞬时能量比作为颤振特征。最后基于瞬时能量比上升量确定颤振监测阈值,判断当前加工状态。试验结果表明,在不同的轧辊磨床加工条件下,所提方法均能在颤振过渡阶段将其检测出来,更快地实现颤振早期预警;与EMD等传统时频分析方法相比,在早期颤振监测中具有明显的优势。

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.

基于EMD-AVOA-BP的逆变器故障诊断方法

以CRH3C型动车组逆变器中的绝缘栅双极型晶体管(IGBT,Insulated Gate Bipolar Transistor)双管开路故障为研究对象,提出了一种基于非洲秃鹫算法(AVOA,African Vultures Optimization Algorithm)和优化的反向传播(BP,Back Propagation)神经网络的逆变器故障诊断方法。在Simulink中搭建列车逆变器的控制模型,取得故障电流;采用经验模态分解(EMD, Empirical Mode Decomposition)对电流信号进行去噪和故障特征提取,再利用AVOA对BP神经网络进行优化,实现了对列车逆变器IGBT双管开路故障的诊断。与传统方法进行对比可知,该方法具有更高的精准度,在测试集中其精准度达到100%。

基于EMD-LSTM人工神经网络的云冈石窟环境参数预测

环境参数会直接影响石窟的风化过程,因此,预测环境参数是进行云冈石窟有效保护的重要内容.以云冈石窟第十窟为例,将壁温、环境湿度、环境温度的实测时序数据作为环境参数,使用经验模态分解(empirical model decomposition,EMD)对实测时序数据进行分解,研究了固有模态函数(intrinsic mode function,IMF)分量与实测时序数据的相关性,建立了基于EMD-长短期记忆(long short-term memory,LSTM)的人工神经网络(artificial neural network,ANN)组合模型.使用平均绝对误差(mean absolute error,MAE)、均方根误差(root mean square error,RMSE)、平均绝对百分比误差(mean absolute percentage error,MAPE)、决定系数(R2)作为评价指标,对比分析了使用组合模型与使用单一LSTM的ANN模型进行环境参数预测的效果.结果表明:IMF分量的变化速率越大,与实测时序数据的相关性就越强;对于组合模型中的LSTM网络模型,当隐藏层层数和初始学习率分别取2和0.001时,组合模型预测效果最优;与单一LSTM的ANN模型相比,使用基于EMD-LSTM的ANN组合模型,环境参数的MAE、RMSE、MAPE值减小、R2值增大,模型预测精度提高;环境参数预测效果主要受环境参数变化幅度的影响,变化幅度越小,组合模型预测效果越好.研究成果对于石窟文物保护具有一定的参考价值.

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.

基于EMD与机器学习算法的近零能耗建筑负荷预测方法

采用皮尔逊相关系数法分析了不同特征变量与冷热负荷的相关性,确定了预测模型的输入特征变量。采用经验模态分解(EMD)对逐日冷热负荷按频分解,然后采用机器学习算法,即反向传播神经网络(BPNN)、随机森林(RF)和支持向量机(SVM),分别对不同频率的负荷量进行了训练、验证,最后重构得到了近零能耗建筑预测负荷。基于上述方法,以北京市某近零能耗居住建筑为研究对象,比较了不同算法预测结果的精确度。结果表明:采用EMD与RF算法相结合对近零能耗建筑冷热负荷的预测精确度较高。进一步采用穷举搜索法对模型初设参数进行了优化,冷热负荷预测结果精确度提高,冷负荷预测结果的决定系数R2、平均绝对百分比误差MAPE分别为0.996、1.32%,热负荷预测结果的R2、MAPE分别为0.997、0.79%。

基于TVFEMD-IMF能量熵增量的桥梁监测数据降噪方法

针对桥梁监测数据受多重噪声干扰、影响结构真实响应获取的问题,提出了一种基于时变滤波经验模态分解(time-varying filtering empirical mode decomposition,简称TVFEMD)和本征模函数(intrinsic mode function,简称IMF)能量熵增量的桥梁监测数据降噪方法。首先,利用TVFEMD分解桥梁原始监测数据,得到多个子序列;其次,采用IMF能量熵增量确定多个子序列中的有效子序列;然后,划分子序列中的结构响应分量和噪声分量,对结构响应分量重组实现监测数据降噪;最后,利用平均绝对误差(mean absolute error,简称MAE)、均方根误差(root mean squared error,简称RMSE)和信噪比(signal-noise ratio,简称SNR)对不同方法的降噪效果进行评价。仿真算例和工程实例结果表明:TVFEMD相比经验模态分解(empirical mode decomposition,简称EMD),有效解决了模态混叠问题;TVFEMD结合IMF能量熵增量方法,有效抑制了多重噪声影响,对结果精度有较大提升;与EMD-IMF能量熵增量和Kalman滤波降噪法相比,TVFEMD-IMF能量熵增量法所得到降噪信号的MAE和RMSE值分别提升了23%和21%以上,降噪效果更好,信噪比提升38%以上,抗噪性能更佳。

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.

基于CEEMDAN功率分解的火电厂混合储能容量优化配置

为了解决火电机组跟随自动发电量指令(Automatic generation control, AGC)响应延迟大、超调大等问题,提出一种基于完全噪声辅助聚合经验模态分解(Complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN)的混合储能系统容量优化配置方法。首先,通过某时间段火电机组跟随AGC指令输出曲线,获得混合储能系统需要提供的功率。在此基础上,利用CEEMDAN将需求功率进行分解,获得不同频率下火电机组与AGC指令之间的误差。选择合适的储能元件,构建火电厂响应AGC指令的混合储能系统结构模型,在考虑能量型储能元件磷酸铁锂电池与功率型储能元件飞轮储能系统两类不同储能设备工作特性的情况下进行功率分配。在考虑储能系统荷电状态(State of charge, SOC)、容量与充放电功率等约束下,建立以综合成本最小为目标的容量优化配置模型,将功率分解结果与容量配置模型联合优化,获得最优功率分配情况和对应的储能配置方案。提供工程案例分析,结果表明所提方法可以有效弥补火电机组跟随AGC指令的延迟功率响应,提高火电机组供电可靠性和经济效益,同时相较于单一储能元件,本方案所设计混合储能系统拥有更优经济性。