基于稀疏指标的优化变分模态分解方法

针对复合信号源信号数目未知,无法正确预设分解模态数K值而不能对信号进行有效变分模态(variational mode decomposition,VMD)的问题,提出了一种基于稀疏指标的优化VMD法。该方法基于VMD所构建变分模型中各个分量的稀疏先验知识,实现了VMD自适应寻优K值,其将最佳K值确定为稀疏指标由上升至下降的转折点;在计算VMD各个分量的稀疏度时,考虑到不同分量间的能量差异加入了能量权值因子,最后将稀疏指标确定为分解后各分量边际谱稀疏度的平均值。仿真信号与实际信号分解试验验证表明:相较于其他两种VMD的K值确定方法,该方法确定的K值结果更为准确,实现的优化VMD自适应性更强,较其他信号分解法如经验模态分解(empirical mode decomposition,EMD)有更好的分解效果,为源信号数目未知的复合信号VMD提供了新思路;此外,噪声的鲁棒性试验证明所提基于稀疏指标的优化VMD法还具有一定的抗噪能力,较稳健,可开发应用于实际工程。

基于自适应变分模态分解的佤语孤立词共振峰估计

基于变分模态分解算法实现对语音共振峰的提取,针对其存在共振峰合并和虚假峰值2个主要问题,通过对分解模态数、平衡约束参数的分析,提出了自适应变分模态分解法.并从的正交性、能量保存度2个方面证明了该方法的可行性;最后,利用该方法实现对佤语共振峰的估计.实验结果表明,基于自适应变分模态分解对佤语孤立词的共振峰估计平均正确率可达85.50%.

The filtering characteristics of HHT and its application in acoustic log waveform signal processing

Array acoustic logging plays an important role in formation evaluation. Its data is a non-linear and non-stationary signal and array acoustic logging signals have time-varying spectrum characteristics. Traditional filtering methods are inadequate. We introduce a Hilbert- Huang transform （HHT） which makes full preservation of the non-linear and non-stationary characteristics and has great advantages in the acoustic signal filtering. Using the empirical mode decomposition （EMD） method, the acoustic log waveforms can be decomposed into a finite and often small number of intrinsic mode functions （IMF）. The results of applying HHT to real array acoustic logging signal filtering and de-noising are presented to illustrate the efficiency and power of this new method.

Random noise attenuation by f–x spatial projection-based complex empirical mode decomposition predictive filtering

The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.

Reservoir detection based on EMD and correlation dimension

In hydrocarbon reservoirs, seismic waveforms become complex and the correlation dimension becomes smaller. Seismic waves are signals with a definite frequency bandwidth and the waveform is affected by all the frequency components in the band. The results will not define the reservoir well if we calculate correlation dimension directly. In this paper, we present a method that integrates empirical mode decomposition （EMD） and correlation dimension. EMD is used to decompose the seismic waves and calculate the correlation dimension of every intrinsic mode function （IMF） component of the decomposed wave. Comparing the results with reservoirs identified by known wells, the most effective IMF is chosen and used to predict the reservoir. The method is applied in the Triassic Zhongyou group in the XX area of the Tahe oil field with quite good results.

Noise-assisted MEMD based relevant IMFs identification and EEG classification

Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation.For a finite set of multivariate intrinsic mode functions(IMFs) decomposed by NA-MEMD,it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way,and conventional approaches address it by use of prior knowledge.In this work,a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance(JSD) measure.A criterion of effective factor based on JSD was applied to select significant IMF scales.At each decomposition scale,three kinds of JSDs associated with the effective factor were evaluated:between IMF components from data and themselves,between IMF components from noise and themselves,and between IMF components from data and noise.The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.

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.

A bearing fault feature extraction method based on cepstrum pre-whitening and a quantitative law of symplectic geometry mode decomposition

In order to extract the fault feature of the bearing effectively and prevent the impact components caused by bearing damage being interfered with by discrete frequency components and background noise,a method of fault feature extraction based on cepstrum pre-whitening(CPW)and a quantitative law of symplectic geometry mode decomposition(SGMD)is proposed.First,CPW is performed on the original signal to enhance the impact feature of bearing fault and remove the periodic frequency components from complex vibration signals.The pre-whitening signal contains only background noise and non-stationary shock caused by damage.Secondly,a quantitative law that the number of effective eigenvalues of the Hamilton matrix is twice the number of frequency components in the signal during SGMD is found,and the quantitative law is verified by simulation and theoretical derivation.Finally,the trajectory matrix of the pre-whitening signal is constructed and SGMD is performed.According to the quantitative law,the corresponding feature vector is selected to reconstruct the signal.The Hilbert envelope spectrum analysis is performed to extract fault features.Simulation analysis and application examples prove that the proposed method can clearly extract the fault feature of bearings.

VMD改进GFCC的情感语音特征提取

传统特征提取忽略了语音信号的非稳态特性,变分模态分解技术可以精细刻画语音的非平稳性,因此利用该技术将情感语音信号分解为K个固有模态函数,对每个分量做快速傅里叶变换后进行频率合成,通过Gammatone滤波器取能量对数,经离散余弦变换得到新特征变,即分模态分解改进Gammatone频率倒谱系数。通过支持向量机进行语音情感识别,实验结果表明,TYUT2.0中的识别率为72.15%,柏林情感语音库中的识别率为91.10%,识别效果优于传统情感语音特征,验证了该特征的有效性。

Diagnosis of Valve-Slap of Diesel Engine with EEMD-EMD-AGST Approach

A hybrid of ensemble empirical mode decomposition and empirical mode decomposition (EEMD-EMD) is introduced to diagnose the valve-slap vibration signal,which is relative to the dominant combustion knock vibration signal given out by a diesel engine around the top dead center (TDC).The time-frequency representations of intrinsic mode functions (IMFs) decomposed by EEMD-EMD are obtained by adaptive generalized S transform (AGST).A type 493 diesel engine was used for the experiment,and the result indicates that the valve-slap of the diesel engine is serious,and the vibration frequencies are higher than the combustion knock.With EEMD-EMD-AGST approach,the valve-slap can be identified by the vibration analysis of the diesel engine.

Single Trial Detection of Visual Evoked Potential by Using EMD and Wavelet Filtering Method

Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical meanings. This paper studies the single trial extraction of visual evoked potential by combining EMD and wavelet threshold filter. Experimental results showed that the EMD based method can separate the noise out of the event related potentials (ERPs) and effectively extract the weak ERPs in strong background noise, which manifested as the waveform characteristics and root mean square error (RMSE).

On-line chatter detection using servo motor current signal in turning

Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.

Detection of Chondromalacia Patellae by Analysis of Intrinsic Mode Functions in Knee-Joint Vibration Signals

This paper presents the knee-joint vibration signal processing and pathological localization procedures using the empirical mode decomposition for patients with chondrom alacia patellae.The artifacts of baseline wander and random noise were identified in the decomposed monotonic trend and intrinsic mode functions (IMF) using the modeling method of probability density function and the confidence limit criterion.Then, the fluctuation parts in the signal were detected by the signal method turning for count. The results demonstrated that the quality of reconstructed signal can be greatly improved, with the removal of the baseline wander(adaptive trend) and the Gaussian distributed random noise. By detecting the turn signals in the artifact-free signal, the pathological segments related to chondrom alacia patellae can be effectively localized with the beginning and ending points of the span of turn signals.