Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise

In practical engineering applications, useful information is often submerged in strong noise and the feature information is difficult to be extracted. Aimed at the detection problem of multi-frequency signal under colored noise background, a novel weak signal detection method based on stochastic resonance (SR) tuning by multi-scale noise is proposed. Firstly, noisy signal is processed by orthogonal wavelet transform to decompose the signal into multi-scale ingredients. According to the orthogonal wavelet transform coefficients characteristics of 1/f distribution, multi-scale noise is constructed so as to make the frequency-band containing the driving frequency be enhanced through SR system. Thus multi-frequency weak signal is detected. The method is effective to detect multi-frequency weak signal under colored noise background. Experiment signal analysis results show that the proposed method is simple for multi-frequency weak signal detection, and has good prospects for engineering applications.     

基于脉冲耦合神经网络模型的小波自适应斑点噪声滤除算法

分析了维纳滤波原理和脉冲耦合神经网络(PCNN)模型的特点,根据斑点噪声统计模型的特征,结合小波变换方法,提出了一种基于PCNN模型的小波自适应斑点噪声滤除算法(W-PCNN-WD)来改善超声图像质量.首先,对超声图像进行对数变换,使斑点噪声转换为加性噪声;对医学图像进行维纳滤波处理,计算其加性噪声的标准方差,并以此作为小波阈值.然后,利用小波变换对图像进行预处理,利用PCNN在小波域中对小波系数进行相应的修正.最后,进行小波逆变换和指数变换,获得滤除噪声的图像.结果表明:本文提出的滤波方法优于其他滤波方法,当噪声方差为0.01时,本文滤波算法获得的峰值信噪比(PSNR)比经Wiener滤波方法获得的高出9 dB.该滤波方法能在有效去除超声斑点噪声的基础上保留图像的边缘细节信息,极大地改善了图像的视觉质量.     

改进的Hilbert-Huang变换及在电磁辐射测量中的应用研究

提出了一种基于改进的希尔伯特-黄变换的电磁信号处理的新方法,该方法适合于在非平稳非线性噪声环境中的电磁辐射的测量。将非平稳信号通过经验模态分解的方法分解为有限个内蕴模式函数,利用自回归模型消除了希尔伯特-黄变换产生的边界效应,进而得到信号的瞬时频率。应用匹配滤波器对背景噪声进行滤除,得到实际电磁辐射信号。由于经验模态分解法的基函数是由信号自适应分解得到的,所以比傅里叶变换以及小波变换得到更好的分解效果。仿真及实验结果表明该方法在非平稳非线性的电磁信号处理中有效地滤除了背景噪声,解决了电磁辐射测量中的环境干扰问题。     

基于经验模分解的陀螺信号去噪

陀螺随机漂移是影响寻北精度的重要因素,小波消噪方法对小波基和分解尺度等因素依赖性较强。提出了一种新的基于功率谱密度准则的经验模态分解(EMD)去噪方法,可有效解决传统EMD去噪自适应滤波器截止阶数难以确定的难题,该方法将经验模态分解得到的固有模态函数(IMF)分为信号分量起主导作用模态与噪声分量起主导作用模态,并对噪声分量起主导作用的模态进行类似小波软阈值去噪的方法进行滤波,然后与信号分量起主导作用的模态共同对信号重建实现去噪。将该方法应用于测试信号与陀螺信号的去噪,结果表明:新方法能有效地判断噪声与信号起主导作用的模态分界点,具有良好的去噪效果,且不受主观参数的影响,具有自适应性。     

SWT based separation method for periodic signal with non-stationary noise and its application in EMF

Periodic signal superimposed with strong non-stationary noise that follows an approximate 1/f distribution cannot be easily separated with traditional signal processing methods. Using the stationary wavelet transform, the noisy signal is decomposed into wavelet coefficients including both the detail and approximate coefficients. According to its periodic feature, detail coefficients on each scale are extracted to form the same-phase sequences which consist of coefficients with the same phase values in each cycle. The amplitude probability distribution functions of same-phase sequences follow approximate Gaussian distribution. Therefore, noise in the same-phase sequences can be removed with the non-linear median filter and moving average filter. Since non-stationary noise follows approximate 1/f distribution, the approximate coefficients on the lowest frequency level have strong non-stationary property. Due to spectrum leakage of different frequency sections, the leakage signal components are superimposed on the approximate coefficients. Three different filtering methods are proposed to process the approximate coefficients in order to extract the useful signal components and to reconstruct the periodic signal accurately. Finally the proposed method is used to process the output signal of electromagnetic flowmeter during slurry flow measurement under different slurry concentrations and different flow rates. Results show that the proposed method is effective in the separation of periodic signal and strong non-stationary noise which follows the approximate 1/f distribution.     

A new denoising algorithm for MFL data obtained from seamless pipeline inspection

The magnetic flux leakage (MFL) nondestructive evaluation technique is used extensively for in-service inspection of gas and oil pipelines. Unfortunately, the MFL data obtained from seamless pipeline inspection is usually contaminated by various sources of noise, which considerably reduces the detectability of defect signals in MFL data. In this paper, a new denoising algorithm is presented for removing seamless pipe noise (SPN) and system noise contained in MFL data. The algorithm first utilizes the new wavelet domain adaptive filtering method proposed by combining wavelet transform with the adaptive filtering technique to remove SPN contained in MFL data and then exploits the coefficient denoising approach with wavelet transform to cancel the system noise in the output of the wavelet domain adaptive SPN cancellation system. Theoretical analysis shows that the proposed denoising algorithm has a better overall performance than the existing denoising algorithm. Results of application of the proposed algorithm to MFL data from field tests are presented to demonstrate the performance of the proposed algorithm compared with the existing denoising algorithm. The text was submitted by the author in English.     

基于小波除噪和经验模式分解的信号分析方法

经验模式分解是一种自适应分解算法。通过对常见信号的经验模式分解结果进行分析,发现信号中包含的噪声对分解结果影响较大。在此基础上,提出一种小波除噪与经验模式分解相结合的信号分析方法。该方法充分利用小波变换的降噪功能和经验模式分解的自适应分解能力,能真实地反映信号特征,为基于信号分析的故障诊断提供了一种可行的途径。     

基于几何纹理与Anscombe变换的蜂窝材料太赫兹图像降噪模型

为解决太赫兹（Terahertz,THz）图像内泊松高斯混合噪声导致芳纶纤维蜂窝材料脱粘缺陷轮廓检测精度低的问题,基于Anscombe变换与小波阈值法构建了THz图像降噪模型。高斯噪声方差为降噪模型的必要参数,但实际THz图像噪声分布未知,且噪声与纹理在高频混叠,给方差准确估计提出了挑战。为此,首先以样件纹理几何形状为先验信息,构造Benzene-ring算子去除THz图像纹理,使其小波域高频分量中仅含有噪声;然后提出改进的Logistic混沌映射提高样本集的多样性,以训练Elman神经网络准确建立高频分量与高斯噪声方差间映射关系;最后依据噪声方差估计值,基于Anscombe变换将泊松高斯混合噪声转化为高斯噪声,并利用小波阈值法与Anscombe逆变换得到了最终THz降噪图像。仿真与试验结果表明,所提出的方法降噪效果最佳并有效提高缺陷轮廓检测精度,相比于高斯滤波、小波阈值以及非局部均值法,平均梯度指标分别提升12%、33%、9%,缺陷面积绝对误差分别降低234 mm²、304 mm²、263 mm²。     

经验模态分解在管道缺陷漏磁检测信号处理中的应用研究

针对管道缺陷漏磁检测信号中存在严重噪声干扰的问题，将经验模态分解方法用于漏磁检测信号的噪声分离和有效信号提取，对实际测试的与输油管道材质相同且具有人为模拟缺陷的漏磁信号进行处理，结果表明，该方法可以很好地抑制噪声从而得到清晰的、表征缺陷特征的有用信号，达到与小波变换相同的处理效果，同时克服了小波方法中基函数选择困难的问题。     

基于二代小波的光纤陀螺实时降噪方法研究

在以光纤陀螺为主要惯性敏感元件的捷联惯导系统中,陀螺输出信号中的非确定性随机漂移的实时滤除,对提高实际系统的初始对准精度及导航精度均具有重要的意义。考虑到传统小波阈值法的去噪性能及实时性问题,提出了一种基于第二代小波变换,并结合硬阈值、强制降噪和带滑动数据窗的光纤陀螺信号实时降噪改进方案。利用MATLAB进行了正弦信号和光纤陀螺输出信号的模拟实时降噪实验,并与一代小波实时降噪方案进行了比较,验证了改进方案在理论计算速度大幅提升的前提下,降噪性能得到提高,进而减小了系统输出的姿态误差。     

SF6断路器气体泄漏红外图像中散斑噪声的抑制算法

SF6断路器是电力系统中常见的电气设备,针对SF6断路器气体泄漏红外图像中广泛存在的激光散斑噪声,提出一种抑制散斑噪声的新算法.将同态均值滤波与小波变换结合,并应用贝叶斯软阈值,对红外图像进行去噪处理.研究中,将该算法与同态均值滤波、同态均值滤波与传统周定小波阈值结合滤波等算法作了比较,结果表明,在图像标准偏差、信噪比和散斑指数等常用性能指标上所提算法优于其他算法.     

纺织丝线张力信号实时消噪技术的研究

提出了基于提升框架的小波变换算法在丝线张力信号实时消噪过程中的应用,给出了该算法的流程图,通过对仿真信号与实测信号的消噪试验,验证了该算法的有效性,该算法可以应用于对实际信号的消噪处理,能够满足对张力信号处理的实时性要求,且易于实现。     

基于小波分析的FastICA联合降噪方法在滚动轴承故障诊断中的应用研究

采用小波分析方法进行振动信号降噪存在选取参数依靠经验的问题,采用独立分量分析（ICA）方法进行振动信号降噪存在欠定问题,为了避免小波降噪以及ICA方法单独使用的缺点,提出了将小波降噪分析和基于负熵的FastICA独立分量分析相结合来处理滚动轴承含噪振动信号的方法。首先对原始信号进行小波降噪处理,然后将处理后的信号与原始信号组成FastICA的输入矩阵,进行FastICA降噪处理,最后利用滚动轴承振动信号对该方法进行有效性验证。实验分析表明：该方法增大了振动信号的峭度值,达到了滚动轴承振动信号降噪的目的。     

基于小波域维纳滤波的光纤面板暗影检测

通过分析光纤面板透光图像的噪声性质,提出在小波去噪的始末对图像取对数、指数变换,成功转换噪声模型,有效去除高斯及斑点噪声.并提出在小波域采用维纳滤波算法以增强去噪功能,实现更为有效地去除光纤面板透光图像中的噪声.最后对去噪图像进行暗影检测,实验结果表明,采用此算法进行去噪,检测出的暗影定位更加精准,冗余信息大量减少,有...     

An EMD threshold de-noising method for inertial sensors

Random errors of inertial sensors are key factors in influencing the performance of Inertial Navigation System (INS). Based on underlying white noise model, classical wavelet threshold de-noising method is incapable of eliminating colored noise. Since time-correlated colored noise is predominant, fractional Gaussian noise (fGn) is utilized to model sensor errors and the Hurst parameter of fGn is estimated by the periodogram method. Variances of the noise in Intrinsic Mode Functions (IMFs) decomposed by Empirical Mode Decomposition (EMD) are analyzed. The standard deviations of noise in the first tow IMFs are estimated by a robust estimator, and then the noise variances in other IMFs can be obtained after the variance relation among the IMFs decomposed from fGn is derived. Noise thresholds of IMFs are estimated through the obtained variances and an EMD threshold de-noising method using order-dependent thresholds is established. The method is firstly verified by a simulation example and then applied in INS and compared with wavelet de-noising method. Results show that wavelet threshold de-noising is poor at suppressing colored noise while EMD threshold de-noising is effective on reducing sensor errors due to its close association with proper noise model.     

一种基于提升算法的自适应小波变换及应用

为了克服传统小波变换的不足,提出了一种用样本相关性检测信号特征的自适应小波变换降噪方法.该方法以第二代小波变换为基础,用变换样本与相邻样本之间的相关性来检测信号的局部特征,并根据相关系数的大小来确定每一尺度上的每个样本的最佳预测器和更新器,使小波能够较好地适应信号的局部特征.在信号相关性强的情况下,采用了最优插值估计的改进算法.模拟实验和工程应用的结果表明,该方法克服了传统小波变换降噪方法丢失原始信号局部信息的缺陷,不仅可以有效地去除原始信号中的噪声,而且能够保留原始信号的局部特征.     

基于EMD和支持向量机的柴油机故障诊断

为了解决传统小波或小波包变换方法对柴油机振动信号频率分辨率不高、易受邻近谐波分量间交叠影响的缺陷,提出了一种基于经验模态分解和支持向量机的故障诊断方法。该方法首先对振动信号进行经验模态分解,分别提取能量最大的几个基本模式分量的小波包特征;然后采用支持向量机在每个独立的特征子集中进行训练,并按该子集对应的基本模式分量的能量权重进行加权融合。试验中将该方法应用于6135型柴油机的故障诊断,结果表明,针对每个基本模式分量分别进行故障分析是可行的,能够对6135型柴油机常见故障模式进行准确识别。     

利用信号相关性抑制光纤陀螺强度噪声

提出了一种利用陀螺干涉信号和耦合器空闲端信号相关性估计光源强度噪声抑制效果的方法,并在现场可编程门阵列(FPGA)中进行了实时估计.根据估计结果,判决是否进行强度噪声相减,以提高光纤陀螺强度噪声抑制方法的可靠性.理论分析表明,强度噪声抑制效果与信号相关性直接相关.利用该方法,对某高精度干涉型光纤陀螺进行了实验.结果表明,当陀螺干涉信号和耦合器空闲端信号互相关系数为0.91时,干涉信号噪声方差降低至17.16％,然而,当上述互相关系数为0.28时,噪声相减法反而使干涉信号噪声方差增大至143.18％,由此验证了理论分析结果.利用该方法可以在线检测陀螺干涉信号和耦合器空闲端信号的相关性,进而避免噪声相减法中当陀螺干涉信号和耦合器空闲端信号相关性较差时,光纤陀螺噪声不降反升的情况,提高了强度噪声相减法的可靠性.     

DT-CWT相关滤波在齿轮箱故障诊断中的应用

根据小波系数的相关分析理论,提出了基于双树复小波变换的小波相关滤波法。该方法根据相邻层小波系数的相关性,通过迭代过程自适应地进行滤波,能够在达到良好降噪效果的同时保留微弱故障特征信息。对降噪后的信号进行希尔伯特包络分析便可准确得到故障特征频率。试验信号分析与工程应用结果表明,该方法能够有效提取强背景噪声下的齿轮箱轴承早期故障特征信息。     

对偶树复小波阈值降噪法及在机械故障诊断中的应用

为有效提取强噪声背景下微弱故障信号,提出了一种基于对偶树复小波的阈值降噪方法及其小波滤波器的设计原则,将其应用于机械故障诊断,取得了较好效果.阐述了对偶树复小波变换滤波器的设计要求和对偶树复小波阈值降噪法的实施步骤.该法充分利用了对偶树复小波变换的平移不变性的优良特性,试验表明:此法可以获得比常规的离散小波降噪更高的信...