Data fusion algorithm for pulsed eddy current detection

A weighted data fusion algorithm based on matching pursuit (MP)-wavelet packet (WP) atomic decomposition and its applications in pulsed eddy current (PEC) non-destructive testing systems for estimation of feature parameters is presented. MP-WP atomic decomposition is used to estimate each noise-free pulse response from its noisy observation of a single-sensor PEC probe and obtain the peak value parameter from each estimated response. A weighted data fusion algorithm, on the basis of minimum mean square error (MMSE), is applied to fuse each obtained peak value together to get final optimum parameter estimation. Based on the difference of each noisy pulse response and its estimation, the variance of noise in each pulse response can be computed, respectively. Accordingly, the weight of each pulse response for data fusion is calculated by the variance of its noise. Finally, the peak value parameter is estimated by the utilised data fusion algorithm. In terms of MMSE, this weighted fusion presents an optimum estimation of the feature parameter of multi-pulse responses of PEC sensor, compared with normal averaging process.     

基于小波分析的惯性传感器信号Kalman滤波

针对光电跟踪系统惯性传感器信号特点,本文提出通过小波分析的方式确定相关Kalman滤波的模型及参数.该方法利用小波分析的优良特性,采用先将信号进行去噪处理,然后对去噪后的信号进行AR建模.根据小波去噪后的信号比较接近真实信号,将得到的观测噪声方差乘以一个小于1的系数后作为系统的过程噪声方差,从而确定模型的噪声参数.仿真实验结果表明,该方法不仅对惯性传感器的静态数据有很好的效果,而且对其动态观测数据也有良好的效果.同时,该方法不仅对光电跟踪系统有效,而且还具有一定的通用性.     

改进的二阶统计量盲分离算法

利用二阶统计量(不同时延相关矩阵)的非平稳性和信号时序结构特征,能简单估计出线性瞬时混合的盲源信号。但随时延[τ]增大,仅利用某一个时延协方差均衡化,忽略了信号的时间变化特性,很难保证算法的性能。通过分析矩阵的平均特征,提出一种改进的基于二阶统计量盲源分离算法,对一组均衡化的时延相关函数进行等时延分段,并对等间隔段的两个时延矩阵分别求取均值,采用类似联合近似对角化,估计出最优化的酉矩阵,最终得到信源的稳健估计。性能指标分析和仿真实验证明,算法很好地解决原算法的不足,并成功分离出信号。     

基于小波分解的时变信道盲辨识

针对传统的信道盲辨识算法很难适应时变信道模型的问题，提出了一种基于小波多分辨率分解的时变信道盲辨识算法．该算法首先应用小波多分辨率分解原理来近似建模时变信道；然后，利用信道输出序列的二阶统计量对时变信道的小波分解模型的小波系数进行两级盲辨识；最后，应用估计的小波系数即可重构时变信道的冲激响应．仿真结果表明，该算法可很好地完成周期时变信道的盲辨识与盲均衡任务．     

基于时变NARMA模型的非线性时变系统辨识

在非线性自回归滑动平均模型NARMA(NonlinearAutoRegressiveMovingAverage)中引入时间变量,将其扩展为时变NARMA模型,用Taylor展开将模型中的非线性函数展开为关于输入输出的多项式,得到关于参数线性时变的多项式形式的时变NARMA模型,再用基序列拟合模型的时变参数得到关于参数线性时不变的模型,最后用递推最小二乘法估计模型参数。仿真算例证明,与小波网络方法相比,辨识精度高,计算量小。     

Analysis of Barkhausen noise using wavelet-based fractal signal processing for fatigue crack detection

The paper presents new approach to Barkhausen noise signal processing for detection of fatigue crack. Barkhausen noise signal from mild steel samples under axial fatigue is investigated using fractal signal processing, particularly wavelet variance method. Based on repeatability analysis new algorithm is developed and applied to acquired signals. The influence of fatigue on fractal characteristics of Barkhausen noise is analyzed. Signal analysis reveals significant and repeatable changes in wavelet variance, spectral parameter and estimated Hurst exponent just after crack initiation. The results demonstrate high potential of fractal analysis of Surface Barkhausen noise applied to fatigue crack initiation detection.     

基于小波多分辨率分析的时变结构参数识别研究

提出一种小波多分辨率分析的最优尺度选择方法,并将其应用于结构时变物理参数的识别。首先,从函数空间剖分的角度引入WMRA对时变参数进行多分辨率近似展开,将振动微分方程转化成多元线性回归方程,根据时变参数的频率范围及采样频率、线性方程组的个数等确定分解层数取值范围;其次,利用赤池信息准则（AIC）寻求最优分解尺度,为增强数据的稳定性,采用正交最小二乘算法（OLS）代替传统最小二乘算法（LS）对模型中小波系数进行估计并重构时变参数;最后,分别以突变和连续变化的两种时变参数的5层剪切框架模型进行数值模拟。分析结果表明:在预先确立的分解尺度范围内,采用无噪声干扰的响应信号进行识别时,识别精度随着分解尺度的增加而增加;采用噪声干扰的测量信号进行识别时,识别精度与分解尺度的增加无必然联系;通过选择适当的分解尺度,能够准确识别时变参数、提高方法的计算效率并保证很好的抗噪性能。     

Wavelet packet based channel equalization

Recently, considerable amount of attention is being given to the field of wavelets and wavelet packets. It has found numerous applications in signal representation, image compression and applied mathematics. In this paper, we present a channel equalization method based on wavelet packets. The proposed equalizer structure is based on the fact that for sufficiently narrowband sequences, a non-ideal channel can be modelled as an attenuation and delay. If the data sequence is used to modulate a set of narrowband wavelet packets, then no equalization is required at the receiver end. The equalization problem reduces to that of determining the delay introduced by the channel for each of the wavelet packets. A minimum square variance algorithm for adaptively choosing the delay has been proposed. This algorithm has been shown to perform as desired analytically in a simple delay channel case. Simulations have been used to study its performance in the non-ideal channel’s case and the results corroborate theoretical predictions.     

R-D匹配优选小波包零树图像压缩

为使R-D小波包优选算法与零树编码在量化精度上更好地匹配，对R-D小波包优选算法的寻优次序进行修改，使小波包各节点的量化精度差异不超过一位。实验结果表明，R-D匹配优选小波包零树编码可以取得与R—D优选小波包零树编码相当或比后者更好的图像压缩效果，可见R-D匹配优选算法是更适合零树编码的小波包优选算法。     

一种基于滑窗TLS-ESPRIT算法的超谐波动态分析方法

提出一种基于滑窗TLS-ESPRIT算法的超谐波动态分析方法。首先,对一定长度的超谐波电压、电流信号施加矩形窗函数,将其截取为等间隔连续的小数据块,对每个小数据块构建空间矩阵并估计超谐波个数;然后,利用TLS-ESPRIT算法估计每个小数据块所含超谐波的频率和衰减因子,再采用最小二乘法估计超谐波的幅值和相位;最后,以三维形式展示超谐波频谱,实现对超谐波的动态分析。数值仿真分析以及2种非线性负荷实测数据验证结果表明:该方法不仅能准确估计出超谐波的个数、频率、衰减因子、幅值和相位等信息,并具有较高的频率分辨率,还能从三维展示中观测到超谐波的时变特性,为深入研究超谐波提供了一种更精确的测量方法。     

非高斯背景下的频域广义匹配检测

采用广义匹配滤波器在功率谱未知的非高斯噪声中检测宽带信号。在估计功率谱时,为了减小周期图法谱估计产生的误差,采用平滑周期图法来估计非高斯噪声的功率谱,并在此基础上对信号进行恒虚警概率损失下的广义匹配检测。实验表明,用平滑周期图法估计功率谱并进行的广义匹配检测,其检测性能稍弱于功率谱取真值时的检测,但远远优于传统的匹配滤波器。     

Determination of the dispersion curve of the refractive index by the stockwell transform

The determination of dispersion curves by the Stockwell transform (ST) is proposed. The required numerical data for dispersion curves are produced from the transmittance signal by the ST. The obtained data are then used to estimate the unknown dispersion parameters in Cauchy's formula using the genetic algorithm method through the minimisation problem based on the least-squares curve fitting. The results have shown that the dispersion curve obtained from the ST produced more improved outcomes than those obtained from the continuous wavelet transform.     

小波域高斯混合模型方差估计近红外降噪方法

针对抑制近红外光谱噪声与保留光谱信号细节的矛盾,提出一种基于噪声方差估计的小波域降噪方法.该法对光谱信号小波域高频系数建立了两状态高斯混合模型,用EM算法估计模型系数,推证模型对噪声方差准确估计特性,将估计得到的噪声方差建立了阈值降噪模型.实验建立黄酒近红外光谱快速预测酒精度偏最小二乘模型,对比分析Penalty阈值、...     

小波消噪阈值算法优化

在研究小波固定阈值消噪方法,讨论阙值估计经验公式的基础上,针对加性噪声小波变换后的统计特征,提出了一种自适应闽值优化算法,该算法可以较准确地估计噪声水平,在消噪的同时保留语音信号中的弱特征成分。通过仿真实验验证,优化算法较传统算法能够更加有效地消除语音噪声,获得最大信噪比。     

基于多尺度Kalman数据融合滤波

本文通过分析基于小波变换的动态系统模型,提出一种基于小波多尺度的Kalman数据滤波方法,本文利用小波的多尺度特点,把初始估计序列多尺度分解,并在不同尺度层上进行Kalman滤波估计,再利用小波重构来融合各层的估计信息,把标准Kalman滤波只在单一尺度和时间轴上对状态估计值和误差协方差进行数据更新,改进为基于小波变换的尺度轴和时间轴上的双向数据更新,该算法将小波多尺度分解去噪和Kalman滤波相结合,对实际中含较强噪声的动态系统的状态估计效果较好.算法也可用于多分辨率多传感器数据融合.     

噪声方差自适应修正的混合系统故障诊断方法

针对混合系统故障诊断问题,提出了一种模型噪声方差自适应修正的多模态故障诊断方法。首先,在粒子滤波的框架内将混合系统故障诊断建模为最优状态估计与跟踪问题,利用实时观察信息和各个模态先验的转移概率,估计最优的故障模态,并针对估计结果进行单独的建模分析;接着,根据平滑估计值和当前观测信息之间的相关性,建立噪声方差在线自适应检测机制,对模态噪声方差进行自适应更新,有效克服了模型噪声统计特性时变对滤波精度的影响,提升了算法的鲁棒性。最后,针对多种模态估计跟踪进行了充分的仿真分析,验证了本文方法的有效性和鲁棒性。     

Wavelet variance, Allan variance, and leakage

Wavelets have recently been a subject of great interest in geophysics, mathematics and signal processing. The discrete wavelet transform can be used to decompose a time series with respect to a set of basis functions, each one of which is associated with a particular scale. The properties of a time series at different scales can then be summarized by the wavelet variance, which decomposes the variance of a time series on a scale by scale basis. The wavelet variance corresponding to some of the recently discovered wavelets can provide a more accurate conversion between the time and frequency domains than can be accomplished using the Allan variance. This increase in accuracy is due to the fact that these wavelet variances give better protection against leakage than does the Allan variance     

基于小波包变换和时变频率的结构地震损伤评估

基于频率变化的结构地震损伤评估方法具有机理明确和精度较高等特点,但传统信号分析方法在时频分辨率上不能同时满足精度要求,导致时变频率不能直接从响应信号中精确获取,影响频率法损伤评估的应用。依据时频边缘条件提出时频谱分析精度评价标准,通过对比不同的信号分析方法,确认具有特定基函数的小波包变换是获取精确时变功率谱的有效工具。提出基于小波包脊的时变频率提取方法,在此基础上依据结构频率的变化可计算结构时变损伤指标,并最终实现结构多维地震损伤评估。算例表明基于小波包变换和时变频率的结构地震损伤评估方法可以较准确地反映结构的整体损伤演变过程和最终损伤程度。应用该方法时仅需要结构的位移时程,在结构动力分析、抗震验算以及实际结构的震害评估中均具有良好的适用性。     

一种基于小波特征熵的液压系统故障信号去噪方法

针对故障诊断的需要和信号特点,提出一种基于小波特征熵的去噪新方法。该方法基于小波特征熵判断有用信号所在频带,重构所在频带系数,得到去噪信号。将该方法和阈值去噪法应用到液压系统压力信号中,去噪结果证明该方法比阈值去噪法更有效。     

Nonlinear Model Selection—Application to an Indirect Inductive Measurement of Conductivity

An efficient method and algorithm for experimental data processing based on parametric inversion is proposed. This method is applied to a metallic rod conductivity measurement based on induced secondary voltage technique. First, the case of an ideal excitation circuit is studied. For this case, when the excitation signal is a current step, a model of the data can be obtained in a closed-form as an infinite sum of exponential functions whose relaxation times are related to the physical properties of the inspected material. Therefore, for time points greater than the largest relaxation constant, only one term is sufficient; but, for these times, the signal-to-noise ratio (SNR) is smaller, so the variance of the estimated conductivity is larger. When taking into account some data at earlier time points, N terms are necessary to minimize the residual modelling error. The conductivity variance is then smaller but the identification process is more complex. A tradeoff has been achieved between these two aspects. Finally, the choice of an L1 norm criterion upon the identification error is used to reject outliers from experimental data. Second, a more realistic circuit is considered (the primary time constant is taken into account). In that case, two steps are necessary to calculate the direct model. A nonlinear equation is solved and the results are put into a closed-form expression (an infinite sum of exponential functions). For both cases, a multiparameters inversion is eventually used to minimize the effect of the modifying quantities