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.

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。

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。

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。

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。

噪声环境中多轨道数字音频信号降噪方法

为提升多轨道数字音频信号的峰值信噪比,降低噪声,使音频更加清晰,提出噪声环境中多轨道数字音频信号降噪方法。使用二进小波分解多轨道数字音频,将信号分解为不同的频率子带,使得噪声和信号在频率域上分离。通过模极大值计算分解信号的奇异性,由于信号和噪声在相同奇异性时的变化不同,因此能够确定信号中的噪声特点,利用层间相关搜索法找到分解后信号中的噪声并去除,最后使用交替投影法将去除噪声的分解信号重构,得到去除噪声的多轨道数字音频信号。实验结果表明:使用该方法进行降噪后,存在噪声的信号幅值得到了控制,一些单独突出可认定为噪声的信号基本消失;对低峰值信噪比的信号降噪,平均峰值信噪比提升了28 dB。

光纤传感器非周期低频动态信号相位载波解调方法研究

为了提高光纤传感器非周期低频动态信号的相位载波解调效果,提出光纤传感器非周期低频动态信号相位载波解调方法研究。采用自适应波束形成算法增强非周期低频动态信号的细节信息;通过奇异值分解算法提取信号的特征;将提取的特征输入到基于双扩展卡尔曼滤波的CD3S信号解调模型中,通过卡尔曼滤波结构剔除噪声;通过联合估计实现动态信号的扩频码同步,完成光纤传感器非周期低频动态信号相位载波解调。实验结果表明,所提方法在有无噪声干扰下解调误码率均处于1.0%以下,解调后位移误差低至12 pm,提高了解调效果和抗噪声能力。

基于大数据挖掘的光通信微弱信号检测研究

以高效、准确检测噪声淹没下光通信微弱信号为目的,设计基于大数据挖掘的光通信微弱信号检测方法。通过基于改进EMD与奇异值分解全面去除光通信信号中噪声分量,经灰狼算法寻优设置支持向量机参数后,由支持向量机模型构建信号分类超平面,分类检测样本中微弱信号。实验结果表明:光通信信号经所提方法去噪后,信号信噪比变小,最大值仅有0.01 dB;所提方法所检测的微弱信号波动幅值,与微弱信号实际幅值高度匹配,误差不超过1%,可100%检测出噪声淹没下光通信微弱信号的样本。

An Improved Singularity Computing Algorithm Based on Wavelet Transform Modulus Maxima Method

In order to reduce the hidden danger of noise which can be charactered by singularity spectrum, a new algorithm based on wavelet transform modulus maxima method was proposed. Singularity analysis is one of the most promising new approaches for extracting noise hidden information from noisy time series . Because of singularity strength is hard to calculate accurately, a wavelet transform modulus maxima method was used to get singularity spectrum. The singularity spectrum of white noise and aluminium interconnection electromigration noise was calculated and analyzed. The experimental results show that the new algorithm is more accurate than tradition estimating algorithm. The proposed method is feasible and efficient.

Random seismic noise attenuation by learning-type overcomplete dictionary based on K-singular value decomposition algorithm

The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.

基于奇异值分解方法的轴承故障振动信号降噪分析

奇异值分解方法的重构矩阵构型和奇异值阶次选取会对信号降噪效果产生影响,任意选取重构矩阵构型和奇异值阶次会导致轴承振动信号的奇异值分解降噪效果不佳。为探究不同重构矩阵构建方法和奇异值阶次选择方法对轴承故障振动信号的降噪效果,文章选取2种重构矩阵构造方法和4种奇异值阶次选取方法,对轴承故障仿真信号进行降噪分析,发现选用Hankle矩阵和奇异值差分选择方法处理后的降噪信号信噪比最大,均方根误差最小。选用该方法对实际轴承故障信号进行降噪处理,Hankle矩阵奇异值差分选择方法对实际轴承故障信号降噪可以取得较好效果。

基于峭度原则的VMD-SVD微型电机声音信号降噪方法

微型电机运转时的声音信号包含丰富的状态信息,可用于生产线上电机的快速检测,但由于待测电机体积小、声音能量低,采集过程中声音信号易与环境噪声耦合,导致声音信号提取和检测不准确。该文通过研究电机组成结构,分析声音信号频率成分与成因,得到该文研究电机的声音信号3倍频谐波特点,提出一种基于峭度原则的VMDSVD算法对电机声音信号进行提纯降噪,该算法采用VMD分段原理,对各分段信号进行SVD分解,提取谐波特征,利用峭度原则优化VMD参数选取。首先通过仿真信号对比实验,验证了该文算法具有更好的降噪效果和降噪性能指标。而后,将该方法应用于微型电机实测声音信号,测试结果表明提出的基于峭度原则VMD-SVD算法具有良好降噪效果,能够显著提高原始信号信噪比,更利于后续特征提取和故障检测工作。

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.

基于DMD降噪的滚动轴承故障诊断方法

针对轴承振动信号难以剔除噪声的问题,提出一种将Hilbert变换、动力学模态分解(DMD)和奇异值差分谱相结合的滚动轴承故障诊断方法。首先将原始信号进行Hilbert变换得到包络信号,由包络信号构造Hankel矩阵进行动力学模态分解,利用奇异值差分谱确定合适的截断秩后进行信号重构,最后通过频谱分析来提取故障特征。采用该方法对滚动轴承故障仿真信号和实验数据进行分析,结果表明其降噪效果显著,能有效获取轴承故障特征频率。

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.