小波包变换和相关性分析用于轴承故障诊断研究

针对滚动轴承振动信号的特点,文章首先对振动信号进行小波包变换,得到不同的分解频带。再对各频带信号与原信号进行相关性分析,求出各信号相关因子作为频带选择的参考。然后对选择的频带信号重构,并进行包络解调,确定出相应的频谱或功率谱,最后与轴承故障特征频率作比较,从而实现故障诊断。对滚动轴承内环故障的实验结果表明,该方法能较好的判断出轴承故障。     

小波变换在压力容器声发射信号中的特征提取

由于压力容器工作环境的影响,通常压力容器声发射故障信号完全被噪声淹没。由于FFT很难提取出故障信号的特征,本文根据声发射信号的特点和小波变换基本原理及特性。应用小波变换与FFT相结合的方法,完全能提取出声发射故障信号的特征。     

电力系统中的通信光纤曲线分析

目前电力系统中的通信大多采用光纤方式,而光纤曲线比较复杂,当出现故障时不容易确定故障点。本文利用小波变换在奇异信号检测和信号滤波方面的优势,给出了光纤故障监测的解决方案,实现了在强噪声干扰下的事件点的准确检测。利用对小波域系数的阈值去噪和信号增强相结合的方法,充分抑制噪声,同时增强突变信号;不但可以准确地在强噪声干扰的情况下检测出事件点,还能精确地确定事件点的位置。     

小波变换高分辨率层序地层学研究

以大庆长垣杏树岗地区高三组层序地层划分为例,分析了小波变换在高分辨率层序划分中的应用。利用对自然伽马曲线进行Morlet小波变换,将测井曲线信号转换为深度与尺度域的变化关系,得到不同尺度上的小波系数曲线,然后建立最佳尺度因子下的小波系数曲线的周期性振荡特征与各级层序界面的对应关系,最终将最佳尺度因子下的小波系数曲线用于层序的识别与划分。同时,结合地震资料和储层反演等资料验证,实践证明,最佳尺度因子下的小波系数曲线能准确识别出各级层序的界面,因此,利用小波变换进行层序地层划分为高分辨率层序地层学研究了提供一条新的途径。     

金属拉伸实验数据小波变换数字滤波方法研究

小波变换具有良好的时频局部化能力。当采用小波变换对金属拉伸实验数据进行数字滤波时,不但能滤除干扰信号,而且能很好的保留信号的突变部分,体现信号原貌,这一功能是傅里叶变换所无法比拟的。通过比较傅里叶变换与小波变换对金属拉伸实验数据的滤波结果,表明采用小波变换处理含噪的实验数据,具有良好效果。     

小波奇异熵算法诊断电子互感器故障研究

目前针对电子互感器故障诊断算法大多以互感器采样数据分析为基础[1],而互感器采样数据与电网运行数据是紧密联系的,导致其算法无法区分电网故障与互感器故障。本文使用的故障诊断算法基于小波变换,使用多尺度模块综合处理的方法获取信号奇异点及其奇异时刻,然后采用小波奇异熵算法,利用电网各种异常状况下电气量信号与电子式互感器故障时电气量信号的区别来检测互感器的突变故障。结合这两种算法可以实时判断电子式互感器的运行状况。     

潜油电机测试装置性能研究

本文对潜油电机的测试系统进行设计,通过所设计的测试系统全面检测潜油电机的各项性能指标进行多次试验。所做的测试通过潜油电机型式试验数据分析系统进行数据的采集,通过系统对试验数据进行分析后,将分析结果导出通过多项式拟合方式、拉格朗日方程和牛顿方程拟合方式绘制拟合曲线。通过对这些数据以及曲线的分析,可以判断被检产品是否符合设计要求、品质的优劣以及改进的目标和方向。     

基于声波信号小波包奇异谱熵和支持向量机的断路器故障诊断研究

在断路器投切时伴随着较强的声波信号,它包含了大量断路器状态信息,通过分析声波信号能够特征提取方法,能够为后续故障诊断提供依据。断路器声波信号易受环境因素影响,且包含非线性、非平稳成份,文中利用小波包奇异谱熵的信号特征提取方法对其进行处理,同时使用支持向量机进行故障识别判断。     

小波分析在汽车领域的应用

发动机作为汽车的核心,其"健康状态"将直接影响汽车的安全使用性,减少交通事故的发生概率,对发动机的诊断维修研究具有重要的意义。文章提出的诊断维修方案,是通过采集仪器收集发动机在正常运行、进气堵塞、排气堵塞三种工况下的振动信号,运用小波包"降噪"对信号进行预处理,然后,对信号进行时域与频域的分析,最后,通过小波包能量谱分析比较发动机在不同工况下的振动信号。从分析对比结果中可以有效判断发动机故障特征频带,同时,为发动机进一步故障监测和诊断提供了对比参考依据。     

一种基于小波变换的PSK信号调制样式识别的新方法

利用PSK信号的不同相位突变点的小波变换产生不同大小冲激值的特点,对信号进行Haar小波变换,通过求取各冲激值的累计分布函数来判别相位突变值的数量,以达到识别PSK信号调制样式的目的。实验证明该方法识别效果良好,具有较大的实用价值。     

Specification error in multinomial logit models: Analysis of the omitted variable bias

In this article, we analyze the omitted variable bias problem in the multinomial logistic probability model. Sufficient, as well as necessary, conditions under which the omitted variable will not create asymptotically biased coefficient estimates for the included variables are derived. Conditional on the response variable, if the omitted explanatory and the included explanatory variable are independent, the bias will not occur. Bias will occur if the omitted relevant variable is independent with the included explanatory variable. The coefficient of the included variable plays an important role in the direction of the bias.     

A new approximation of the posterior distribution of the log–odds ratio

In this paper, the posterior density of the log–odds ratio is studied. It is assumed that the observations have a multinomial distribution and that the prior on the multinomial parameters is a Dirichlet density. Several approximations currently available are reviewed. Under certain conditions on the prior parameters of the Dirichlet density, it is shown that the posterior moments can be computed exactly. A new approximation, similar to the Edgeworth expansion is also proposed. Using a numerical example, the different methods of approximation of posterior density are compared.     

The combined inverse binomial sampling procedure

In the Combined Inverse Binomial Procedure (CIB), independent Bernoulli trials are performed one after another until for the first time either a fixed number of successes or a fixed number of failures are obtained. The sample size is the minimum of a pair of negative binomial variables. The distribution of the sample size and its expectation as well as the problems of estimation and testing of hypotheses in CIB are dealt with. It is seen that tests based on CIB procedures can lead to savings in sample size as compared to fixed sample size procedures in certain situations. It is also briefly indicated how the theory can be extended to the case of combined inverse multinomial procedures.     

Goodness of fit for seemingly unrelated regressions: Glahn's R2y.x and Hooper's r̄2

This paper presents a measure of goodness of fit for Zellner's seemingly unrelated regressions. The measure is a monotonic transform of the appropriate (asymptotic) F-statistic, is bounded on [0, 1] and is maximized by Zellner's estimation technique. Glahn's composite correlation coefficient is shown to be a special case of this measure. It is also compared to Hooper's squared trace correlation coefficient. All three measures as well as some additional asymptotic summary test statistics are calculated for the two-equation example of Zellner. The applicability of the latter test statistics seems not to be recognized in applied work.     

板式换热器常见故障分析及改进性研究

板式换热器是热力系统中的常见设备,它的常见故障有介质外漏,介质进、出口压差大,介质混合,冷却效果不明显四种形式。本文从多方面对板式换热器的故障原因进行了探讨和分析,阐述了处理故障的方法,并提出了预控措施。论证了板式换热器有必要进行改进,得出了扩展传热面积,加大传热温差,提高传热系数均能强化换热设备的传热效果。     

SELECTION BIAS CORRECTIONS BASED ON THE MULTINOMIAL LOGIT MODEL: MONTE CARLO COMPARISONS

Abstract.  This survey presents the set of methods available in the literature on selection bias correction, when selection is specified as a multinomial logit model. It contrasts the underlying assumptions made by the different methods and shows results from a set of Monte Carlo experiments. We find that, in many cases, the approach initiated by Dubin and MacFadden (1984) as well as the semi-parametric alternative recently proposed by Dahl (2002) are to be preferred to the most commonly used Lee (1983) method. We also find that a restriction imposed in the original Dubin and MacFadden paper can be waived to achieve more robust estimators. Monte Carlo experiments also show that selection bias correction based on the multinomial logit model can provide fairly good correction for the outcome equation, even when the IIA hypothesis is violated.     

An application of nonparametric volatility estimators to option pricing

We discuss the impact of volatility estimates from high frequency data on derivative pricing. The principal purpose is to estimate the diffusion coefficient of an Itô process using a nonparametric Nadaraya–Watson kernel approach based on selective estimators of spot volatility proposed in the econometric literature, which are based on high frequency data. The accuracy of different spot volatility estimates is measured in terms of how accurately they can reproduce market option prices. To this aim, we fit a diffusion model to S&P 500 data, and successively, we use the calibrated model to price European call options written on the S&P 500 index. The estimation results are compared to well-known parametric alternatives available in the literature. Empirical results not only show that using intra-day data rather than daily provides better volatility estimates and hence smaller pricing errors, but also highlight that the choice of the spot volatility estimator has effective impact on pricing.     

Microeconometric models of tourists'' destination choice

The focus of this paper is on the microeconometric analysis of spatial choice in a cross section. Nested multinomial logit models are used to analyze the determinants of individual choice among destinations and vacation activities. Cramer and Ridder's likelihood ratio test for pooled alternatives in multinomial logit models is sequentially applied in order to determine the adequate aggregation level of the mutually exclusive alternatives in the choice set. The specification test suggested by Chesher and Santos Silva (1992) is used to investigate whether or not the IIA proposition is fulfilled within the assumed choice subsets.     

Portfolio optimization in a defaultable market under incomplete information

We consider the problem of maximization of expected utility from terminal wealth in a market model that is driven by a possibly not fully observable factor process and that takes explicitly into account the possibility of default for the individual assets as well as contagion (direct and information induced) among them. It is a multinomial model in discrete time that allows for an explicit solution. We discuss the solution within our defaultable and partial information setup, in particular we study its robustness. Numerical results are derived in the case of a log-utility function, and they can be analogously obtained for a power utility function.