基于评分法的测量系统能力评价

目前所用的测量系统能力评价方法是建立在正态性假设的基础上,如果测量系统误差不满足正态性,运用原来的评价指标在对测量系统能力进行评价的过程中会发生误判。将连续等级概率评分(CRPS)应用到测量系统分析评价当中,通过研究CRPS的相关特性,建立了基于CRPS评分法的测量系统能力评价指标,从而使测量系统误差非正态分布时,对测量系统能力进行评价的过程中不会发生误判。通过案例分析表明,当测量系统误差为正态分布时,运用CRPS和运用传统评价指标给出的评价结果是一致的,同时,CRPS评价方法能够有效实现非正态测量系统误差情形下的测量系统能力评价。     

一种基于结构动态特性的物理参数识别算法及应用

结合经典数学理论均匀设计和回归分析,借助有限元计算软件和动态测试技术,提出一个简单实用的结构物理参数识别算法,该算法是基于频域法识别物理参数的一种方法。大量的数值计算和工程应用表明,当系统具有良好可重复性和稳定性,待识别参数为一元或二元时,该算法能够有效地解决这类系统中的参数识别问题。利用该算法对某钢桁架结构边界条件中的部分连接刚度进行识别,得到了比较满意的结果,并验证了该算法解决实际问题时的有效性和可行性。     

一种基于数字摄影测量系统的校准方法研究

随着数字摄影测量系统在工业制造领域应用的不断深入,对其准确性的评价要求日益增加。本文研究了国外的校准方法,基于摄影测量系统的分类,结合国内校准的需求和条件,分析了国外目前采用的校准方法存在的问题和不足之处。提出一种切实可行的对摄影测量系统准确性进行校准的方法,并通过试验和数据分析,对该方法进行了论证。     

基于广义置信区间的测量系统能力比较

研究了测量系统分析中对测量同一产品质量特性的两个测量系统能力的比较方法。基于广义推断法,提出了一种比较两个测量系统能力的方法。通过重复性与再现性（R&R）试验研究,利用广义推断法对两个测量系统能力的比较指数建立相应的广义置信区间,并进行计算机仿真。结果表明,基于广义置信区间的方法,可以有效地比较两个测量系统的能力。     

基于方差 —相关分析的测量系统有效分辨力评价方法

本文基于二分法、方差分析和相关分析的思想，提出了一种便捷地评价测量系统有效分辨力的方法，并应用这种方法对机器视觉测量系统的有效分辨力进行了评价。     

多元统计分析的模态参数辨识方法比较及应用

工作模态参数辨识是实现飞行器结构精细化设计和安全评估的关键基础问题。基于结构响应数据,利用盲源分离和流形学习的方法进行系统模态参数辨识,建立基于多元统计分析的工作模态参数辨识方法。首先,从主成分分析(PCA)、独立成分分析(ICA)和局部线性嵌入(LLE)算法出发,建立响应模态坐标表示与多元统计分析算法之间的内在联系,将模态参数辨识问题转化为基于结构响应数据的多元统计分析求解问题。然后,设计1个离散3自由度系统和搭建1个悬臂板典型实验结构系统,获取数值仿真和实验响应数据。最后,基于测量的响应数据,利用多元统计分析方法辨识系统参数,并分析比较3种不同方法的模态参数识别精度以及抗噪性能。数值仿真和实验结果表明,提出的多元统计分析方法能够有效识别出系统的模态振型和模态频率,且LLE算法较其他两种方法具有更高的识别精度和鲁棒性。     

基于理化指标的葡萄酒质量评价研究

确定葡萄酒质量时一般是通过聘请一批有资质的评酒员进行品评,这主要是根据感官进行评价,本文基于葡萄酒及酿酒葡萄的理化指标,通过数学统计分析的方法来对葡萄酒的质量进行评价,针对4个问题分别建立了显著性差异分析及基数型可信度模型、聚类分析模型、一元及多元回归分析模型、灰色关联度模型对问题进行求解,对每个问题给出了计算结果和结论分析,并结合计算结果说明了所建模型的合理性。     

刚体-弹性支承系统振动解耦评价方法分析

刚体-弹性支承系统多自由度振动的耦合程度评价方法是进行系统固有振动解耦设计的重要基础。对已有的基于耦合刚度、基于振型向量和基于系统动能分布的三种模态振动解耦评价方法进行了系统的分析,指出了各种方法的特点和不足之处,进而采用系统广义惯性力或广义弹性力做功的概念,提出了基于系统振动模态示功向量的振动解耦程度评价方法,具有物理意义准确、量纲统一的优点,适于评价具有不同类型振动响应量的模态振动的解耦程度,是对原有基于系统动能分布的评价方法的完善和推广。并列举两例来说明所提方法的有效性及其物理概念的严密性。     

测量视觉系统MTF的一种客观方法

视觉系统的光学调制传递函数( MTF ),通常采用以大脑知觉为基准,对客观物理刺激进行主观评价的心理物理方法来测量。利用哈特曼-夏克波前传感器测量视觉系统的波像差,再由波像差和瞳函数的自相关,即可得出视觉系统的MTF。与心理物理方法相比,该方法不受被测试者情绪和心理状态的影响,并克服了测量过程繁琐的缺陷,具有客观、简便、快捷的特点,测量的全过程仅需3分钟。     

光电半导体封装测量系统能力分析方法研究

为了提高半导体封装分箱工序的质量水平,有必要对测量系统进行：分析与研究。在深入分析光电半导体封装过程中的测量系统特征的基础上,建立了基于属性GR＆R的测量系统能力分析模型和方法,并通过实例进行了验证。     

A Modified Region Approach for Multivariate Measurement System Capability Analysis

Measurement system capability analysis is to determine whether the measurement system is capable for use in quality control. The existing research has been extended from univariate to multivariate cases. Two approaches, the multivariate analysis of variance (MANOVA) and the weighted principal components (WPC), were advocated in literature. The MANOVA method is constructed based on the volume ratio that treats the volume of constant‐density contours as the variability estimations. However, it ignores the fact that the relative position change of multivariate measurement errors could affect the measurement system capability. The WPC method uses dimension reduction to reduce the complexity but is unable to build the precision‐to‐tolerance ratio because it does not include tolerance. In this paper, we propose a modified‐region‐based method to compute the precision‐to‐tolerance ratio, the percent of repeatability and reproducibility, and the signal‐to‐noise ratio. This method also incorporates the variance–covariance structure of the measurement errors when dealing with the constant‐density contours of tolerances, total variation, and process variation. The performance of the modified‐region‐based method is evaluated based on a dataset from the literature and a set of relevant simulation. The proposed method proves to be effective compared with other methods.Copyright © 2014 John Wiley & Sons, Ltd.     

Full Bayesian evaluation of the safety effects of reducing the posted speed limit in urban residential area

Full Bayesian (FB) before–after evaluation is a newer approach than the empirical Bayesian (EB) evaluation in traffic safety research. While a number of earlier studies have conducted univariate and multivariate FB before–after safety evaluations and compared the results with the EB method, often contradictory conclusions have been drawn. To this end, the objectives of the current study were to (i) perform a before–after safety evaluation using both the univariate and multivariate FB methods in order to enhance our understanding of these methodologies, (ii) perform the EB evaluation and compare the results with those of the FB methods and (iii) apply the FB and EB methods to evaluate the safety effects of reducing the urban residential posted speed limit (PSL) for policy recommendation. In addition to three years of crash data for both the before and after periods, traffic volume, road geometry and other relevant data for both the treated and reference sites were collected and used. According to the model goodness-of-fit criteria, the current study found that the multivariate FB model for crash severities outperformed the univariate FB models. Moreover, in terms of statistical significance of the safety effects, the EB and FB methods led to opposite conclusions when the safety effects were relatively small with high standard deviation. Therefore, caution should be taken in drawing conclusions from the EB method. Based on the FB method, the PSL reduction was found effective in reducing crashes of all severities and thus is recommended for improving safety on urban residential collector roads.     

Pinpointing biomarkers in proteomic LC/MS data by moving-window discriminant analysis

The identification of differential patterns in data originating from combined measurement techniques such as LC/MS is pivotal to proteomics. Although "shotgun proteomics" has been employed successfully to this end, this method also has severe drawbacks, because of its dependence on largely untargeted MS/MS sequencing and databases for statistical analyses. Alternatively, several MS-signal-based (MS/MS-independent) methods have been published that are mainly based on (univariate) Student's t-tests. Here, we present a more robust multivariate alternative employing linear discriminant analysis. Like the t-test-based methods, it is applied directly to LC/MS data, instead of using MS/MS measurements. We demonstrate the method on a number of simulated data sets, as well as on a spike-in LC/MS data set, and show its superior performance over t-tests.     

Process analysis,monitoring and diagnosis,using multivariate projection methods

Multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance are becoming more important because of the availability of on-line process computers which routinely collect measurements on large numbers of process variables. Traditional univariate control charts have been extended to multivariate quality control situations using the Hotelling T² statistic. Recent approaches to multivariate statistical process control which utilize not only product quality data (Y), but also all of the available process variable data (X) are based on multivariate statistical projection methods (principal component analysis, (PCA), partial least squares, (PLS), multi-block PLS and multi-way PCA). An overview of these methods and their use in the statistical process control of multivariate continuous and batch processes is presented. Applications are provided on the analysis of historical data from the catalytic cracking section of a large petroleum refinery, on the monitoring and diagnosis of a continuous polymerization process and on the monitoring of an industrial batch process.     

Multivariate statistical process control charts: an overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart‐type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out‐of‐control signal. Copyright © 2006 John Wiley & Sons, Ltd.     

Performance characteristics of a composite multivariate quality control system.

We present the results of an evaluation of the performance characteristics of a composite multivariate quality control (CMQC) system that incorporates quality control rules for univariate, multivariate, and correlation conditions. The CMQC system evaluated is designed to help analysts detect unacceptable trends and systematic error in one or more variables, unacceptable random error in one or more variables, and unacceptable changes in the correlation structure of any pair of variables. It is also designed to be tolerant of missing data, to allow analysts to reject as few as one or as many as all variables in a run, and to provide analysts with control statistics and graphics that logically relate to sources of analytical error. We show that the various components of the CMQC system have adequate statistical power to detect systematic errors, random errors, and correlation changes under the conditions likely to be encountered with multivariate analytical measurement systems: (1) a single variable with increased systematic or random error; (2) all variables or a subgroup of variables affected by a common problem that increases systematic or random error; and (3) missing data for one or more variables in a run. We also show that the power of the multivariate component of the CMQC system to detect systematic and random errors is higher than the power of an alternative multivariate test criterion.     

Independent component analysis applied on gas sensor array measurement data

The performance of gas-sensor array systems is greatly influenced by the pattern recognition scheme applied on the instrument's measurement data. The traditional method of choice is principal component analysis (PCA), aiming for reduction in dimensionality and visualization of multivariate measurement data. PCA, as a second-order statistical tool, performs well in many cases, but lacks the ability to give meaningful representations for non-Gaussian data, which often is a property of gas-sensor array measurement data. If, instead, higher order statistical methods are considered for data analysis, more useful information can be extracted from the data. This paper introduces the higher order statistical method called independent component analysis (ICA) as a novel tool for analysis of gas-sensor array measurement data. A comparison between the performances of PCA and ICA is illustrated both in theory and for two sets of practical measurement data. The described experiments show that ICA is capable of handling sensor drift combined with improved discrimination, dimensionality reduction, and more adequate data representation when compared to PCA.     

Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process

In modern manufacturing processes, massive amounts of multivariate data are routinely collected through automated in-process sensing. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio and missing values. Conventional univariate and multivariate statistical process control techniques are not suitable to be used in these environments. This article discusses these issues and advocates the use of multivariate statistical process control based on principal component analysis (MSPC-PCA) as an efficient statistical tool for process understanding, monitoring and diagnosing assignable causes for special events in these contexts. Data from an autobody assembly process are used to illustrate the practical benefits of using MSPC-PCA rather than conventional SPC in manufacturing processes.     

Measurement System Analysis in a Production Environment with Multiple Test Parameters

The purpose of measurement system analysis (MSA) is to separate the variation among devices being measured from the error in the measurement system. The total measurement system error can be further decomposed into variance components associated with the measurement equipment and repeatability. An analysis of variance approach based on a variance component model is used to model the variables of interest. Once estimated, the variance components are used to compute various metrics, which quantify the adequacy of the measurement system for the application in which it is used. Confidence intervals computed on the variance components and metrics indicate the amount of precision in the estimates. The MSA is typically conducted on a single measurement variable with a single measurement instance. The aim of this paper is to extend the univariate single-instance case to a common manufacturing test scenario where multiple parameters are tested on each device with a sequence of tests, which may include retest and test and repair steps. The methods presented are illustrated with examples from an industrial application.     

SAS System for Elementary Statistical Analysis

This article proposes a two-stage statistical method for the analysis of multivariate computer experiments when at least one of the output dimensions is large. The stage-one data are modeled by a multivariate extension of a widely used scalar statistical model for computer output. Conditioned on stage-one data, a simple statistical model is then proposed for the stage-two data. The method is demonstrated in a geophysical application involving an ocean model.