EEMD自适应去噪在拉曼光谱中的应用

二代小波是公认较好的降噪手段,但是降噪效果依赖于基函数、分解层数和阈值等参数设置。经验模态分解(empirical mode decomposition, EMD)无需参数设定,按照频率特性将信号分解成本征模函数(intrinsic mode function, IMF),对IMF滤波,实现了信号自适应去噪。拉曼光谱中信号和噪声交叠集中在极高频段,EMD产生模态混叠问题,影响去噪效果。应用总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)拉曼光谱克服了模态混叠,有效区分出高频信号和噪声,获得了与小波函数相似去噪效果。文中首先对一段非线性非平稳豆油脂拉曼光谱EMD分解,可见模态混叠,EEMD分解出清晰模态的特征分量。然后分别用快速傅里叶变换(fast Fourier transform,FFT)、小波变换(Wavelet)、EMD和EEMD处理含噪光谱,信噪比、均方根误差、相关系数三个方面指标表明FFT高频去噪效果最差,其次是EMD,恰当的Wavelet同EEMD效果相当,EEMD的优势是降噪过程的自适应。最后提出光谱时频分析方法和IMF噪声属性判别准则研究趋势。     

基于总体平均经验模态分解的光纤周界预警系统模式识别方法

针对光纤周界预警系统输出信号的非平稳特性,提出了一种基于总体平均经验模态分解(EEMD)的模式识别方法。预警系统基于Mach-Zehnder干涉原理,利用4条单模光纤构成分布式扰动传感器,实时监测周界入侵事件。该方法引用具有自适应性的EEMD算法将振动信号分解成多个本征模态函数(IMF)。根据不同振动信号能量各异的特点,提出EEMD能量熵的方法排除非入侵的干扰。最后建立双重支持向量机对入侵信号进行识别。实验结果表明:该方法可以有效排除非人为入侵的干扰,准确识别攀爬、敲击和其他虚警信号,平均正确识别率优于92%,提高了系统的报警识别率,降低了误报率。     

基于DE-LSSVM的滚动轴承故障诊断

针对滚动轴承故障诊断难以获得大量样本的问题以及LS-SVM 模型参数选择方法易陷入局部最优的缺点,提出了一种集合经验模态分解能量熵和差分进化算法（DE）优化最小二乘支持向量机相结合的轴承故障诊断方法。首先原始振动信号采用EEMD分解得到一组固有模态函数（IMF）,从有效本征模态函数IMF分量中提取的能量特征作为输入建立支持向量机,通过计算不同振动信号的能量熵值大小来判断轴承的故障损伤程度。为了提高模型的诊断精度,采用差分进化算法对LS-SVM的结构参数进行优化,并与LS-SVM和PSO-LSSVM模型相比较。结果表明,DE-LSSVM 模型的故障分类准确性得到了提高,可以有效应用于滚动轴承故障诊断中。     

仿生鳍条姿态测量及其误差分析

在复杂环境下齿轮箱信号往往会淹没在噪声信号中,特征向量难以提取。为了有效的进行故障诊断,提出了基于最大相关反褶积(MCKD)总体平均经验模态分解(EEMD)近似熵和双子支持向量机(TWSVM)的齿轮箱故障诊断方法。首先采用MCKD方法对强噪声信号进行滤波处理,在采用EEMD方法对齿轮箱信号进行分解,分解后得到本征模函数(IMF)分量进行近似熵求解,得到齿轮特征向量,最后将其输入到TWSVM分类器中进行故障识别。仿真实验表明,采用MCKD-EEMD方法能够有效的提取原始信号,与其他分类器相比,TWSVM的计算时间短,分类效果好等优点。     

基于MCKD-EEMD近似熵和TWSVM的齿轮箱故障诊断

在复杂环境下齿轮箱信号往往会淹没在噪声信号中,特征向量难以提取;为了有效地进行故障诊断,提出了基于最大相关反褶积(MCKD)总体平均经验模态分解(EEMD)近似熵和双子支持向量机(TWSVM)的齿轮箱故障诊断方法;首先采用MCKD方法对强噪声信号进行滤波处理,在采用EEMD方法对齿轮箱信号进行分解,分解后得到本征模函数(IMF)分量进行近似熵求解,得到齿轮特征向量,最后将其输入到TWSVM分类器中进行故障识别;仿真实验表明,采用MCKD-EEMD方法能够有效地提取原始信号,与其他分类器相比,TWSVM的计算时间短,分类效果好等优点。     

随机噪声对经验模态分解非线性信号的影响

采用Monte-Carlo随机模拟方法来研究外部噪声对经验模态分解非线性信号的影响.结果表明:噪声对低阶特征模态函数(IMF)影响较为明显,对高阶IMF影响较小;白噪声强度系数越大,分解出的IMF纯噪声分量阶数越多;用含噪声信号减去经验模态分解后的主要IMF噪声分量,可较为明显地削弱噪声的影响;含噪声响应的最大Lyapunov指数比不含噪声响应的最大Lyapunov指数小     

EMD与神经网络在气液两相流流型识别中的应用

本文提出了EMD与Elman神经网络相结合的气液两相流流型识别的新方法.将压差波动信号经验模态分解(EMD)后的固有模态函数(IMF)进行分析、提取IMF能量作为Elman神经网络的输入特征向量,对水平管内的气液两相流流型进行识别.实验结果表明:该方法优于BP网络且稳定、识别率高,具有可行性.     

基于复数据经验模态分解的噪声辅助信号分解方法

集成经验模态分解(EEMD)在一定程度上减轻了经验模态分解(EMD)中的模态混叠,但集成平均会带来新的模态混叠、频谱丢失和运算量增大等问题,影响到对信号物理特征的分析与提取.因此,本文提出一种基于复数据经验模态分解(CEMD)的噪声辅助信号分解方法,在CEMD中以白噪声分解的内禀模态函数(IMF)在指定方向上的投影为基函数来辅助观测信号分解过程中的极值选取,从而减小模态混叠,同时利用噪声投影的影响在求包络质心时被消除的特性,减小EEMD因集成平均带来的相关问题.仿真结果表明,本文方法在进一步降低模态混叠效应的同时,明显提高了运算速度,并且在一定程度上减轻了频谱丢失问题.     

闪电回击场多尺度分析

利用聚合经验模式分解(EEMD)方法对闪电负地闪回击电场波形进行了分析并得到一系列具有物理意义的内禀模态函数 (IMF)。 结果表明,IMF1是高频噪声项,趋势项R主要是静电场作用的结果。 IMF2~IMF11表现出闪电通道的不同尺度放电特征。通过计算IMF分形维数,发现越高阶的IMF对应着越大的分形维数,表明越高阶的IMF对应着越曲折的闪电放电通道。相比于基于经验模式分解（EMD）的希尔伯特-黄变换（HHT）谱和小波谱,基于EEMD的HHT谱能改善模态混叠现象,EEMD更适合于观察像闪电这种非平稳非线性信号。     

基于集合经验模态分解和奇异值分解的激光雷达信号去噪

为了提高差分光柱像运动激光雷达(DCIM雷达)探测信噪比,提出了一种基于集合经验模态分解(EEMD)和奇异值分解(SVD)的混合降噪法.由EEMD获得含噪信号多层模态分量,根据各模态分量之间互相关系数的差分量确定主要噪声并予以滤除,利用奇异值分解识别模态分量中的残余噪声并提取有用信号.利用混合降噪法EEMD-SVD和EEMD方法分别对模拟仿真信号和实测激光雷达信号进行降噪处理.结果表明,当模拟噪声标准差在0.05~0.2之间时,相比与未降噪直接反演的湍流廓线,EEMD-SVD方法降噪后反演的湍流廓线信噪比提高了2.718 7dB~6.921 5dB,相应的EEMD方法提高了1.446 1dB~3.366 1dB;两个不同时段DCIM雷达降噪前后反演廓线与探空廓线的对比发现,EEMD-SVD和EEMD两种方法降噪后反演廓线较之于未降噪的反演廓线,信噪比最大提高了2.526 5dB和2.155 6dB.EEMD-SVD的降噪效果优于EEMD,能够更有效地识别和滤除噪声,较大地提高了原始信号的信噪比,获得更准确的大气湍流廓线反演结果.     

集合经验模态分解在区域降水变化多尺度分析及气候变化响应研究中的应用

集合经验模态分解(EEMD)是一种适用于非线性、非平稳序列的信号分析方法, 将EEMD 应用于气候要素时间序列, 可提取可靠真实的气候变化信号, 同时, EEMD可以得到气候变化的固有时间尺度.本文使用EEMD方法, 从气候时间序列中提取气候信号中各个尺度的变化, 对渭河流域过去50年来的秋季降水进行多尺度分析,结果显示, 对于20世纪70年代末80年代初的全球气候突变, 渭河流域的秋季降水也有很好的响应, 而且大尺度上的响应要早于中小尺度, 其中在大尺度上主要表现为波动形式, 即降水距平正负位相持续期的变化, 从持续正位相到正负位相周期性交替出现; 而在中小尺度上主要是振幅大小, 即降水距平正负位相量级的变化, 量级从相对较大变为相对较小再逐渐增大. 关键词： 集合经验模态分解 多尺度变化 秋季降水 渭河流域     

基于改进经验模态分解域内心动物理特征识别模式分量的心电信号重建

心电图(electrocardiogram,ECG)诊断心脏疾病的严格标准,要求有效地消除噪声并准确地重建ECG信号.经验模式分解(empirical mode decomposition,EMD)方法重建ECG信号中,模式混叠及重建采用模式分量的识别以经验为基础,导致重建ECG信号准确度降低,且方法不具有自适应和通用性.本文首先基于积分均值定理提出一种改进的EMD方法——积分均值模式分解(integral mean mode decomposition,IMMD)方法,经5000个高斯白噪声样本的蒙特卡罗法验证,IMMD方法比EMD具有更优多分辨率分析能力,能够有效地缓解模式混叠.其次,基于ECG信号内固有心动物理特征量识别重建ECG信号所采用的模式分量,具有现实物理意义,因此,方法具有自适应和通用性.经验证,提出方法重建47例ECG信号与原ECG信号的相关系数中:31例优于变分模式分解方法;33例优于Haar小波软阈值法;42例优于集总经验模式分解方法;45例优于EMD方法.相关系数均值为0.8904,方差为0.0071,表现稳定且最优.     

Outboard abnormal noise source localization method with curved surface projection based on time delay matching and weighting criterion

An improved localization method consisting of "filtering-time delay estimationhyperbolic localization" is proposed. Combining the empirical mode decomposition(EMD)and time delay estimation method based on generalized average magnitude difference function,the original signals are decomposed into intrinsic mode function(IMF) components. The energy distribution criterion and spectrum consistency criterion are used to select the IMFs, which can represent the physical characteristics of the source signal. Several sets of signals are applied to estimate the time delay, and then a vector matching criterion is proposed to select the correct time delay estimation. Considering the hydrophones location, a shell model is established and projected to a plane according to the quadrant before the hyperbolic localization. Results of mooring and sailing tests show that the proposed method improves the localization accuracy,and reduces the error caused by time delay estimation.     

A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

In this paper,the ensemble empirical mode decomposition(EEMD) is applied to analyse accelerometer signals collected during normal human walking.First,the self-adaptive feature of EEMD is utilised to decompose the accelerometer signals,thus sifting out several intrinsic mode functions(IMFs) at disparate scales.Then,gait series can be extracted through peak detection from the eigen IMF that best represents gait rhythmicity.Compared with the method based on the empirical mode decomposition(EMD),the EEMD-based method has the following advantages:it remarkably improves the detection rate of peak values hidden in the original accelerometer signal,even when the signal is severely contaminated by the intermittent noises;this method effectively prevents the phenomenon of mode mixing found in the process of EMD.And a reasonable selection of parameters for the stop-filtering criteria can improve the calculation speed of the EEMD-based method.Meanwhile,the endpoint effect can be suppressed by using the auto regressive and moving average model to extend a short-time series in dual directions.The results suggest that EEMD is a powerful tool for extraction of gait rhythmicity and it also provides valuable clues for extracting eigen rhythm of other physiological signals.     

利用经验模态分解的高频水声信号滤波方法*

研究了一种高频水声信号的滤波问题,提出了一种改进的经验模态分解加小波阈值滤波方法。首先对信号进行带通滤波处理,再进行经验模态分解,将分解得到的各个模态转换为频域信号,采用小波软阈值方法对这些频域信号进行滤波,最后对信号进行重构,并转换为时域信号。经数值仿真与试验数据验证表明此方法是可行有效的,与原基于经验模态分解的小波阈值滤波方法相比,本方法滤波效果较好:对不同输入信噪比的仿真信号进行滤波后,本方法的输出信噪比最大提高17.41 d B,滤波后所得信号与加噪前纯信号的相关系数最大提高0.90;对实验数据进行滤波后,不同时间段信号的相关系数最大提高0.62。     

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.     

Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis

Accurately identifying faults in rotor-bearing systems by analyzing vibration signals, which are nonlinear and nonstationary, is challenging. To address this issue, a new approach based on ensemble empirical mode decomposition (EEMD) and self-zero space projection analysis is proposed in this paper. This method seeks to identify faults appearing in a rotor-bearing system using simple algebraic calculations and projection analyses. First, EEMD is applied to decompose the collected vibration signals into a set of intrinsic mode functions (IMFs) for features. Second, these extracted features under various mechanical health conditions are used to design a self-zero space matrix according to space projection analysis. Finally, the so-called projection indicators are calculated to identify the rotor-bearing system?s faults with simple decision logic. Experiments are implemented to test the reliability and effectiveness of the proposed approach. The results show that this approach can accurately identify faults in rotor-bearing systems.