小波变换在生产过程监测中的应用

研究利用小波变换提取过程检测信号的频带分布特征,并提出一种新的模式分类算法用于对提取的特征归纳分类,并将分类结果用于实际过程的工况监测.应用实例研究证实了所提方法的可行性和有效性．     

小波分析在热重实验数据处理中的应用

利用小波分解、滤波、重构对热重信号进行去噪处理,消除实验中由于各种因素产生的噪声,得到了较为满意的热重实验数据.该方法与其他处理方法相比,得到的处理信号不失真且去噪效果明显.比较发现小波去噪的结果与阈值选取相关,通过分析确定合适的阈值选取规则,使得处理后的信号能满足以后分析的需要.研究表明选择的阈值可广泛应用于不同煤种的热重实验数据处理.     

泡沫夹层板脱粘损伤的小波分析检测技术研究

研究了一种通过对结构的应变响应进行复高斯二维小波分析来有效识别泡沫夹层板脱粘损伤的检测方法.数值模拟了无损板和多种脱粘损伤情况下的泡沫夹层板在悬臂条件、正弦激励下的应变响应,对损伤前、后的应变响应用复高斯二维小波进行小波变换,得出有损情况与无损情况下相应位置小波变换模极大值的差值,并用三维图表示出来.结果表明,相应位置小波变换模极大值的变化与脱粘损伤有很好的对应关系.小波变换模极大值变化量三维图中的峰值可有效表征脱粘损伤的位置和损伤程度.     

基于小波变换去噪的多元统计投影分析及其在化工过程监控中的应用

引　言化工过程数据的重要特点之一是受噪声污染严重 ,这严重影响了过程信息处理和分析的效果 .例如 ,在运用多元统计过程控制 (MSPC)进行化工过程监控时 ,直接利用这些受到污染的测量信息对过程进行分析 ,必然会导致较大的误差 ,使结果置信度下降 .对于过程故障诊断来说 ,就会产生较高的误报率或漏报率 .为解决这一问题 ,人们运用小波变换对测量信号进行去噪 ,提高了信号的置信度并取得了较　.　好的应用效果[1] .然而 ,通常的小波变换去噪是针对过程测量信号直接进行分频去噪 ,并没有考虑信号间的相关关系 ,因而这一处理方法不能更有效…     

MODELLING FOR THE WAVELET COEFFICIENTS OF ARFIMA PROCESSES

We consider a model for the discrete nonboundary wavelet coefficients of autoregressive fractionally integrated moving average (ARFIMA) processes in each scale. Because the utility of the wavelet transform for the long‐range dependent processes, which many authors have explained in semi‐parametrical literature, is approximating the transformed processes to white noise processes in each scale, there have been few studies in a parametric setting. In this article, we propose the model from the forms of the (generalized) spectral density functions (SDFs) of these coefficients. Since the discrete wavelet transform has the property of downsampling, we cannot directly represent these (generalized) SDFs. To overcome this problem, we define the discrete non‐decimated nonboundary wavelet coefficients and compute their (generalized) SDFs. Using these functions and restricting the wavelet filters to the Daubechies wavelets and least asymmetric filters, we make the (generalized) SDFs of the discrete nonboundary wavelet coefficients of ARFIMA processes in each scale clear. Additionally, we propose a model for the discrete nonboundary scaling coefficients in each scale.     

ASYMPTOTICALLY POINTWISE OPTIMAL RULES IN THE POISSON PROCESS

The concept of asymptotic pointwise optimality in the discrete time processes provided by Bickel and Yahav (1967) is extended to the continuous time processes in the present paper. An asymptotically pointwise optimal (A.P.O.) rule is proposed in the homogeneous Poisson process. It is shown to be asymptotically optimal for the arbitrary priors and asymptotically non-deficient for the conjugate priors, expanding the discrete time processes of Bickel and Yahav (1968) and Woodroofe (1981) to accommodate the continuous time processes, respectively.     

基于小波去噪的陶瓷墙地砖边缘检测

在陶瓷墙地砖的边缘检测过程中,图像在采集、转换和传输中不可避免的会引入噪声,产生降质而影响检测结果。介绍了一种基于小波去噪的陶瓷墙地砖边缘检测方法,对墙地砖图像采用小波去噪后再进行边缘检测,其结果较好地满足了检测要求。     

基于非线性PLS小波基神经网络的动态过程监测

引言对于化工动态过程,当某些过程参数,由于人为的误操作、设备性能的下降、仪表故障以及外部环境的干扰等因素的影响,而偏离最优操作区域较远时,则认为是发生了故障．如果在过程刚偏离正常操作区域时,能够有效地监测出过程异常,就能最大限度地保证过程在优良状态下运行．动态过程监测可以理解为对数据窗内采集到的数据x（t）进行实时分类．本文利用小波变换以及非线性PLS（PartialLeastSquares）算法,提出一种新的基于非线性PLS小波基神经网络的动态过程监测方法,并将其用于动态过程监测．1基于非线性PLS小波基神经网络的动态…     

小波分析在复合材料声发射信号特征研究中的应用

在综合描述复合材料声发射信号特征和小波变换基本原理的基础上,本文总结了小波变换在声发射信号分析中的研究进展,并重点描述小波变换对复合材料破坏过程中声发射信号的特征提取、时频分析和信号除噪及声源定位方面的研究,提出了小波变换在声发射信号分析应用中需要进一步解决的问题。     

紧支集正交子波的构造及其应用

介绍了紧支集子波的特性,详细讨论 Ｄａｕｂｅｃｈｉｅｓ 子波的构造方法并给出应用实例。说明子波变换用于系统响应信号的分析是十分有效的。     

一种用于动态过程故障诊断的子波-S型神经网络

在分析子波变换的基础上,将子波理论引入神经网络,提出了一种新的神经网络模型─—子波-S型神经网络(WSNN),并将其成功地应用于动态过程的故障诊断,同时根据其特性给出训练方法, 实验证明,与S形作用函数的前向阶层型神经网络(SBFN)相比,子波-S型神经网络提高了故障早期诊断的正确诊断率。     

应用小波分析研究外循环反应器的浓度波

应用小波分析研究了外循环反应器浓度波这类非平稳拟周期信号的特征 .首先利用能量最大准则确定浓度波的特征尺度 .其次利用浓度波的特征尺度 ,重构了浓度波的波形 .最后利用小波分析计算了浓度波波峰处的局部奇异性指数     

石材平面磨削温度信号的小波分析

采用热电偶夹丝法测量了平面磨削山西黑石材过程中接触弧区的温度。利用小波分析提取信号中各个频率段的成分信息，探讨了石材平面磨削温度信号的频率特征。比较了傅里叶变换低通滤波、小波强制消噪、默认阚值消噪和给定阀值消噪处理等手段对温度信号的消噪结果，结果表明，小波去噪比傅里叶变换滤波更有利于保存信号的高频有用成分，而确定合理的闽值进行给定软闽值消噪比默认闽值更具有可信度。     

Adaptive wavelet decompositions of stationary time series‡

A general and flexible framework for the wavelet‐based decompositions of stationary time series in discrete time, called adaptive wavelet decompositions (AWDs), is introduced. It is shown that several particular AWDs can be constructed with the aim of providing decomposition (approximation and detail) coefficients that exhibit certain nice statistical properties, where the latter can be chosen based on a range of theoretical or applied considerations. AWDs make use of a Fast Wavelet Transform‐like algorithm whose filters ‐ in contrast with their counterparts in Orthogonal Wavelet Decompositions (OWDs) – may depend on the scale. As with OWDs, this algorithm has good properties such as computational efficiency and invariance to polynomial trends. A property whose pursuit plays a central role in this work is the decorrelation of the detail coefficients. For many time series models (e.g., FARIMA(0,δ,0)), the AWD filters can be defined so that the resulting AWD detail coefficients are all (exactly) decorrelated. The corresponding AWDs, called Exact AWDs (EAWDs), are particularly useful in simulation of Gaussian stationary time series, if the associated filters have a fast decay. The proposed simulation methods generalize and improve upon existing wavelet‐based ones. AWDs for which the detail coefficients are not exactly decorrelated, but still more decorrelated than those of OWDs, are referred to as approximate AWDs (AAWDs). They can be obtained by truncating EAWD filters, or by adopting some of the existing approaches to modeling the dependence structure of the OWD detail coefficients (e.g., Craigmile et al., 2005 ). AAWDs naturally lead to new wavelet‐based Maximum Likelihood estimators. The performance of these estimators is investigated through simulations and from some theoretical standpoints. The focus in estimation is also on Gaussian stationary series, though the method is expected to work for non‐Gaussian stationary series as well.     

小波变换用于采样信号的噪声滤除

介绍了小波变换原理,讨论了小波理论在实时采样信号处理及抗噪声干扰中的应用,仿真结果表明,利用小波变换原理进行采样信号滤波是十分有效的。这对提高系统的控制精度是有利的     

SPECTRAL ANALYSIS OF STATIONARY POINT PROCESSES USING THE FAST FOURIER TRANSFORM ALGORITHM

Abstract. In this paper we consider techniques of spectral analysis for stationary point processes in order to study the behaviour of a complex physiological system. The estimates of the power spectrum are obtained by smoothing the periodogram which is computed very rapidly with the help of the fast Fourier transform algorithm. In the computation of the estimates we can use either the whole record of the data or a number of disjoint records.     

BAYES SEQUENTIAL ESTIMATION OF POISSON MEAN UNDER A LINEX LOSS FUNCTION

This paper considers Bayes sequential estimation of the mean of a Poisson distribution using a LINEX loss function and a cost c > 0 for each observation. Under a Gamma prior distribution, it is shown that an asymptotically pointwise optimal rule is asymptotically non-deficient in the sense of Woodroofe (1981).     

傅里叶变换红外光谱在橡胶研究中的应用

综述了傅里变换红外光谱在橡胶的表征、化学反应及加工使用过程中的应用情况。     

基于二维小波分解的图像压缩方法

基于二维小波分解的图像压缩方法 ,给出了二维正交小波基的构造原理 ,并将二维Mallat算法用于图像压缩处理 ,取得了满意的结果。表明小波分解是实现图像压缩的一种十分有效的工具     

墙地砖边缘检测中的小波去噪

在墙地砖的边缘检测过程中,噪声的引入是不可避免的,有效地消除噪声而不影响检测结果是检测过程中需要解决的关键问题。本文采用了小波去噪方法—对图像进行去噪后再进行边缘检测的方法,其检测结果较好地满足了检测要求。