IMPROVED SINGULAR VALUE DECOMPOSITION TECHNIQUE FOR DETECTING AND EXTRACTING PERIODIC IMPULSE COMPONENT IN A VIBRATION SIGNAL

Vibration acceleration signals are often measured from case surface of arunning machine to monitor its condition. If the measured vibration signals display to have periodicimpulse components with a certain frequency, there may exist a corresponding local fault in themachine, and if further extracting the periodic impulse components from the vibration signals, theseverity of the local fault can be estimated and tracked. However, the signal-to-noise ratios (SNRs)of the vibration acceleration signals are often so small that the periodic impulse components aresubmersed in much background noises and other components, and it is difficult or inconvenient for usto detect and extract the periodic impulse components with the current common analyzing methods forvibration signals. Therefore, another technique, called singular value decomposition (SVD), istried to be introduced to solve the problem. First, the principle of detecting and extracting thesignal periodic components using singular value decomposition is summarized and discussed. Second,the infeasibility of the direct use of the existing SVD based detecting and extracting approach ispointed out. Third, the approach to construct the matrix for SVD from the signal series is improvedlargely, which is the key program to improve the SVD technique; Other associated improvement is alsoproposed. Finally, a simulating application example and a real-life application example ondetecting and extracting the periodic impulse components are given, which showed that the introducedand improved SVD technique is feasible.

Singularity detection of the thin bed seismic signals with wavelet transform

The location of singularities may be detected by local maxima of the wavelet transform modulus. The digital modeling and focusing process to wavelet transform of the reflecting seismic signals have been done. It has been found that the locations of singularities after wavelet transform are only affected by two factors, their original locations and the seismic wavelet length, which says it does not matter with what shape the wavelet will be. The wavelet length can be determined according to the wavelet transform results and be eliminated thereafter so that we are able to detect thin bed seismic signal with resolution of l/32 wavelength. The singularities have been recovered with improved resolution of the seismic section by real data processing.

Study on Singularity of Chaotic Signal Based on Wavelet Transform

Based on the variations of wavelet transform modulus maxima at multi-scales, the singularity of chaotic signals are studied, and the singularity of these signals are measured by the Lipschitz exponent.In the meantime, a nonlinear method is proposed based on the higher order statistics, on the other aspect, which characterizes the higher order singular spectrum (HOSS) of chaotic signals. All computations are done with Lorenz attractor, Rossler attractor and EEG(electroencephalogram) time series and the comparisions among these results are made. The experimental results show that the Lipschitz exponents and the higher order singular spectra of these signals are significantly different from each other, which indicates these methods are effective for studing the singularity of chaotic signals.

基于EMD-SVD的矿山微震信号降噪方法及其应用

为了提高微震监测技术对微震信号分析处理的准确性,充分提取微震信号波形中的有效信息,针对矿山微震信号非平稳、非线性的特点,提出了一种基于经验模态分解(EMD)和奇异值分解(SVD)的联合降噪方法。该方法首先通过EMD分解获得信号的IMF分量,利用相关系数、方差贡献率和相似度对IMF分量进行了优选;然后使用优选后的IMF分量重构一维微震信号时间序列的相空间数据,经过SVD分解后,利用奇异值能量百分比确立了SVD重构阶数,并根据SVD恢复原理得到了降噪后的一维微震时间序列;最后以山东某矿现场矿山爆破为例,采用不同降噪方法对3类典型微震信号进行了降噪处理,并对其降噪效果进行了对比分析。结果表明,EMD-SVD降噪方法与传统降噪方法相比,其平均信噪比提高了35%,平均均方根误差降低了50%,有效剔除了微震信号的噪声分量,保留了信号的特征信息。该研究对分析矿山微震信号、微震事件定位及煤矿动力灾害监测具有重要意义。

基于PE-CEEMD-SVD的Φ-OTDR信号降噪方法

为实现相位敏感光时域反射仪中相位信号的精确测量,提出了一种基于排列熵算法的互补集合经验模态分解联合奇异值分解的新型降噪方法(PE-CEEMD-SVD)。首先,对含有噪声的相位信号进行CEEMD分解,得到一系列频率不同的IMF分量;然后,将PE算法和相关系数机制相结合,保留较大相关的有用分量,对较小相关的噪声分量使用SVD算法进行二次降噪;最后将两次降噪后保留下来的有用分量进行重构。仿真和实验结果表明,相较于EMD、EEMD和CEEMD降噪方法,该方法可获得更高信噪比的信号,有利于相位信号的精确测量。

The Fourier Notation of the Geomagnetic Signals Informative Parameters

The paper discusses the quantitative definition of the s/n (signal to noise ratio) by means of new computational parameters derived (and computed) by the Fourier analysis. The theme is of great relevance when the geomagnetic observed field has high transient noise and high energy content (i.e.geomagnetic signal interfered by human activity magnetic band) and when the signal analysis action is oriented to the detection of magnetic sources characterized by quasi-punctiform size, low energy level and kinetic mechanical status (i.e.uw armed terrorist). The paper shows the results obtained introducing two new informative spectral parameters: the informative capability “C” and the enhanced informative capability “eC”. These parameters are depending on the comparison of the energy of the target signal with total field energy and they are characteristics of each elementary signal. C classifies the energy of the spectrum in two metrological bands: elementary signal informative energy EI (band or single signal) and passive energy EP. This metrological classification of the energy overtakes the concept of noise: each signal is part of the noise band when it is not under observation and becomes out of the band when it is under observation (numerical observation→computation). C (and eC) allows to compute the value of the “visibility” of the informative signals in a high energy geomagnetic field (or spectrum). C is a fundamental parameter for the evaluation of the effectiveness of singularity magnetic metrology in the passive detection of small magnetic sources in high noised magnetic field.

Novel Sampling and Reconstruction Method for Non-Bandlimited Impulse Signals

To sample non-bandlimited impulse signals, an extremely high-sampling rate analog-todigital converters （ADC） is required. Such an ADC is very difficult to be implemented with present semiconductor technology. In this paper, a novel sampling and reconstruction method for impulse signals is proposed. The required sampling rate of the proposed method is close to the signal innovation rate, which is much lower than the Nyquist rate in conventional Shannon sampling theory. Analysis and simulation results show that the proposed method can achieve very good reconstruction performance in the presence of noise.

Approach to blind estimation of the PN sequence in DS-SS signals with residual carrier

This paper presents an approach of singular value de- composition plus digital phase lock loop to solve the difficult problem of blind pseudo-noise （PN） sequence estimation in low signal to noise ratios （SNR） direct sequence spread spectrum （DS-SS） signals with residual carrier. This approach needs some given parameters, such as the period and code rate of PN sequence. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. An autocorrelation matrix is then computed and accumulated by those signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of the autocorrelation matrix. Further more, a digital phase lock loop is used to process the estimated PN sequence, it estimates and tracks the residual carrier and removes the residual carrier in the end. Theory analysis and computer simulation results show that this approach can effectively realize the PN sequence blind estimation from the input DS-SS signals with residual carrier in lower SNR.

脉冲信号的奇异性分布熵特征分析

为了对低信噪比复杂环境下脉冲信号的奇异性差异进行有效的分析和标定,提出了一种基于模极大值理论的奇异性分布熵特征分析模型。首先对脉冲信号进行归一化并进行小波变换,计算各尺度下模极大值及其特定分布,可以体现具有奇异性差异的模极大值曲线族。为定量描述这种差异性,用熵值表达构成模极大值曲线族的模极大值点分布,并构建能有效分析脉冲信号奇异性差异的奇异性分布熵特征模型。该模型能对低噪比下信号的奇异性差异进行刻画。实验结果表明,在信噪比为-6 dB的环境下对典型的直升机脉冲信号(桨/涡干扰信号和高速脉冲信号)进行分析,能够得到89.25%和87.63%的正确率。

基于SVD-K-means算法的软扩频信号伪码序列盲估计

针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。

带残余频偏的软扩频信号伪码序列盲估计

针对带残余频偏的软扩频信号伪码序列盲估计难的问题,提出一种奇异值分解(singular value decomposition,SVD)结合全数字锁相环(digital phase locked loop,DPLL)的方法。所提方法首先对待处理信号通过不重叠分段生成数据矩阵,每段信号长度为一倍伪码周期;然后利用其自相关矩阵的右上角元素估计失步点进行同步,并且在重新计算自相关矩阵后根据较大特征值个数估计进制数;最后通过多次快速SVD算法结合DPLL最终实现伪码序列的盲估计。仿真结果显示,所提方法在低信噪比条件下可以有效估计出带残余频偏的软扩频信号的伪码序列,并且性能优于其他对比方法。

基于小波奇异特征约束的期望最大时延估计算法

针对低信噪比条件下非平稳信号时延估计精度低的问题,提出基于小波奇异特征约束的期望最大时延估计算法。设计小波奇异性特征尺度广义互相关矩阵,构建多尺度小波奇异特征约束下的期望最大化模型。推导参数更新公式,利用期望最大化算法并行迭代,求取奇异性特征显著性最大条件下信号的自适应尺度以及该尺度下声源信号的最优时延估计值。仿真和实验结果表明,所提算法在低信噪比条件下,相较于传统广义互相关时延估计算法以及改进算法具有较高的时延估计精度,并且有效提高了误差约束范围内的有效估计成功率。

基于奇异值滤波的旋转装备转速提取

课题组针对无硬件测量设备的振动信号转速信息提取算法中存在频带不易确定和运算量大等问题,提出一种奇异值分解滤波的转速估计方法。先将振动加速度信号先作Hankel重构,再奇异值分解,然后利用差分奇异谱确定重构奇异分量,依据连续两个奇异分量确定一个频率成分,重构各频率分量信号,最后对重构分量作频谱分析估计当前转速。实验表明:奇异值分解滤波能有效提取信号中的频率分量,进而从振动加速度信号中提取转速信息。对比实验证明了课题组提出的方法具有频带自适应、易于实现和精度高的优势。

强噪声中检测微弱目标信号特征的量子信号处理算法

随着噪声功率的增强,微弱目标信号的特征受噪声污染变得模糊且难以区分,导致微弱信号检测算法失效,提出一种可以保护目标信号特征的量子信号处理方法——局域半经典信号分析算法。详细介绍了算法实现量子化的原理和在量子域中保护目标信号特征的性质;给出算法步骤以及重要参数的计算方式;将所提算法与奇异值分解、小波阈值降噪算法结合进行了仿真分析和实验验证。结果表明,所提算法保护目标信号特征的能力可以帮助降噪算法检测极低信噪比的微弱信号,与其他方法结合可极大改善信噪比,准确提取信噪比为-30 dB的微弱目标信号,算法性能优越。

一种融合KPCA、FastICA及SVD的腹壁源胎儿心电 信号提取算法研究

目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singular value decomposition,SVD)的胎儿心电信号提取算法。方法:首先,采用KPCA对母体心电信号进行降维,再利用改进的基于负熵的FastICA处理降维后的数据,得到独立成分。随后,引入样本熵进行信号通道选择,挑选出包含最多母体信息的信号通道。在选中的母体通道上进行SVD,得到母体心电信号的近似估计,再用腹壁源信号减去该信号得到胎儿心电的初步估计。最后,采用改进的基于负熵的FastICA成功分离出纯净的胎儿心电信号。在腹部和直接胎儿心电图数据库(Abdominal and Direct Fetal Electrocardiogram Database,ADFECGDB)和PhysioNet 2013挑战赛数据库中对提出的算法进行验证。结果:提出的算法在主观视觉效果和客观评价指标上都表现出优越的性能。在ADFECGDB数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.74%、98.85%和99.30%;在PhysioNet 2013挑战赛数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.10%、97.87%和98.48%。结论:融合KPCA、FastICA及SVD的胎儿心电信号提取算法在提取胎儿心电信号的同时有效处理了附加噪声,为胎儿疾病的早期诊断提供了有力支持。

具有奇异灵敏度的趋化模型解的有界性

在齐次Neumann边界条件下,考虑了具有奇异灵敏度和间接信号产生机制的抛物-抛物-ODE趋化模型。当模型中的参数满足一定的条件时,已有文献给出解的整体存在性,而解的一致有界性尚未可知。因此,文中给出该模型解的一致有界性的严格证明。利用该模型已有解的第二个分量的一致下界估计,再运用Young不等式、Gagliardo-Nirenberg不等式、插值不等式等工具得到解的整体存在性和一致有界性的充分条件。

基于滑动窗奇异值分解的局部放电信号检测方法

在局部放电信号检测过程中,局部放电信号幅值小,易受到噪声干扰,导致低信噪比下局部放电信号检测与波形恢复难度大。本文提出了基于滑动窗奇异值分解的局部放电信号检测方法。该方法利用噪声干扰信号与局部放电信号奇异值的差异性,通过滑动窗分段处理,建立奇异值差值序列,达到降噪与还原局部放电信号的目的。仿真与实测结果表明,本文方法在抑制噪声干扰和还原局部放电信号方面相比传统方法更具优势,且稳定性更好,适合在混合噪声干扰下实现对实测局部放电信号的检测和波形恢复。

ISWD-SVD联合方法的变压器振动信号降噪

针对变压器有效振动信号受噪声干扰难以提取的问题,提出一种改进群分解-奇异值分解(Improved Swarm Decomposition-singular Value Decomposition,ISWD-SVD)的变压器振动信号降噪方法。该方法首先将功率谱熵负值作为目标函数,利用麻雀搜索算法(Sparrow Search Algorithm,SSA)寻找群分解算法(Swarm Decomposition,SWD)最优参数;然后,采用最优参数对变压器振动信号进行SWD分解,并剔除剩余分量,得到重构信号;最后,利用SVD去除重构信号中的噪声残留,实现二次降噪。通过对仿真信号与现场信号进行降噪效果验证,并与其他降噪算法进行对比分析。结果表明:ISWD-SVD联合方法对变压器振动信号具有更好的降噪效果,可为变压器机械状态检测和故障诊断提供有力依据。

基于CM-SVDS-SVMD的滚动轴承故障特征提取方法

针对滚动轴承微弱故障特征信息易受噪声干扰提取困难的问题,提出一种新的滚动轴承故障特征提取方法,即协方差矩阵(covariance matrix,CM)、奇异值差分谱(singular value difference spectrum,SVDS)和奇异值中值分解(singular value median decomposition,SVMD)相结合。首先,考虑到旋转机械的故障特征,对轴承故障信号采用1步长方法构造Hankel矩阵;其次,考虑到信号的协方差矩阵对于信号自相关去噪的优势,进而计算Hankel的协方差矩阵并进行空间重构;再次,采用奇异值差分谱方法对重构后的协方差矩阵信号进行分解处理而实现初步降噪,通过奇异值中值分解方法对其进行分解和筛选处理而完成二次降噪,并根据处理后信号的频谱包络,实现轴承故障特征信息的提取;最后,通过滚动轴承仿真数据分析得出,所提方法能够有效提取出噪声信号的故障特征及其谐波,实现不同轴承故障类型特征的有效提取,为滚动轴承故障复杂信号处理和诊断提供了一种新的方法和途径。

基于奇异值分解算法的电气设备绝缘介损传感信号检测方法

针对信号起伏特性冗余,忽略了双重信号噪声的影响,导致检测结果的绝缘介损因数偏差较大的问题,提出基于奇异值分解算法的电气设备绝缘介损传感信号检测方法。构建时序特征窗口平滑处理原始传感信号,分析处理后传感信号的脉冲与高斯双重噪声,分离重构去除相应噪声值,引入奇异值分解算法将绝缘介质电流传感信号分解出来,解析分解信号的形态波计算得到绝缘介损传感信号检测结果。实验结果表明:所提方法应用后得出的检测结果,表现出的绝缘介损因数偏差较小,检测准确度较高,满足了电气设备绝缘性能运维的实际需求。