全变化参数的中位值-极差联合控制图的经济设计

在统计质量控制中,通常利用中位值图和极差图来控制生产过程的均值和方差.建立了全变化参数的中位值和极差联合控制图,同时提出了一种费用函数以提供最优化设计参数的方法,最后通过一个例子说明了该模型能节约成本.     

在固定时间抽样的可变抽样区间控制图

本文根据Reynolds在固定时间抽样的可变抽样区间（VSIFT）的x^-控制图^[1]的模型设计中位值x^-和极差R图，规定样本在样等间隔的固定时间点抽取，当过程有变化的迹象时，允许有两个固定时间之间抽取附加样本，本文计算了VSIFTx^~图和R图及联合x^~-R图的发信号前的平均时间，并同固定抽样区间（FSI）的常规x^~和R图作比较，所设计的VSIFTx^~和R图能缩短过程失控时间从而减少不合格品数。     

可变样本容量和抽样区间的联合～x和R控制图

作者在前文（1）中研究了可变样本容量（VSS）和抽样区间（VSS）和抽样区间（VSI）的中位值（X^～)和极差（R）控制图，指明其功效均大于常规（FSS）控制图。本文将前文的研究方法推广到联合X^～和R图，记作CVSSIX^～-R图，它能更快地发现过程平均值和方差的变化，从而减少不合格品数。     

可变样本容量和抽样区间的联合和R控制图

作者在前文[1] 中研究了可变样本容量 (VSS)和抽样区间 (VSI)的中位值 (～x)和极差 (R)控制图 ,指明其功效均大于常规 (FSS)控制图。本文将前文的研究方法推广到联合 ～x 和R图 ,记作CVSSI～x -R图 ,它能更快地发现过程平均值和方差的变化 ,从而减少不合格品数     

可变样本容量和抽样区间的联合中位值和极差控制图

最近的理论研究表明具有可变样本容量(VSS)和可变抽样区间(VSI)的控制图比常规控制(FSSI)图能更快地揭露生产过程中的问题．本文将以前单个控制图的研究方法推广到联合中位值(x^-)和极差(R)控制图，记作CVSSIx^-—R图．假定过程处于控制状态的时间T服从负指数分布。利用Costa的马氏链方法设计CVSSIx^-—R图。并同联合常规(CFSSI)图作比较．所设计的CVSSI图较之CFSSI图能更快地发现过程平均值和方差的较小和中等的变化，从而减小不合格品数．     

可变参数的联合中位值和极差控制图

最近设计了可变样本容量和抽样区间的联合中位值$(\wt{x})$和极差$(R)$控制图$^{[1]}$, 本文利用Costa的可变参数控制图的方法$^{[2]}$, 设计包括可变控制限的可变参数的联合$\wt{x}$和$R$图(CVP $\wt{x}$--$R$图). 计算了在可变参数下发信号前的平均时间, 并同联合常规$\wt{x}$--$R$图(CFSSI图)和可变样本容量和抽样区间的联合$\wt{x}$--$R$图(CVSSI图)作比较, 所设计的控制图能较快地发现过程平均值和方差的小变化, 提高CVSSI图的效率     

可变抽样区间的质量控制图

本文根据 Reynolds 等的可变抽样区间的(?)-图的模型设计具有可变区间的(?)和R-图.所设计的控制图是当中位值(?)或极差 R 接近于(但不超过)控制限时使用较短的抽样区间;当(?)和 R 接近于目标值(或中心值)时使用较长的区间.若(?)或 R 超出控制限,则与固定区间的控制图一样发出信号.我们要计算在可变抽样区间下发信号前的平均时间和平均样本数,并同固定区间的(?)和 R-图作比较.     

两阶段相关过程的VP均值-残差联合控制图

基于回归残差监控的思想研究了两阶段过程变参数控制图设计的问题.考虑样本容量、抽样区间和控制限全部可变的情况下,采用马尔可夫链的方法,构建了监控过程的可变参数(VP)Z_(?)-Z_(?)联合控制图。以修正的平均信号时间(AATS)为准则,首先利用汽车刹车系统的案例说明了VP Z_(?)-Z_(?)联合控制图的监控效果,然后通过仿真对不同过程参数情形下VP Z_(?)-Z_(?)联合控制图、固定参数Z_(?)-Z_(?)联合控制图和VSSI Z_(?)-Z_(?)联合控制图的监控效果进行了比较分析。结果表明,VP Z_(?)-Z_(?)联合控制图能够更为有效地实现对两阶段相关过程的质量控制.     

样本数较少时的极值控制图

「１」中研究了样本数较少时中位值和极差控制图。本文研究样本数较少时极值控制图的制定方法，给出与极值有关的统计量的分布，建立极值图的控制限，极值图不需计算，有关集中和分散的信息在一个图上给出，且可画上规格限，在实践中应用方便，本文制定的极值控制可应用于小批量生产的生产过程。     

可变抽样区间的单边控制图

利用质量控制图监督生产过程时 ,通常每隔固定时间从过程抽取固定容量的样本。本文在前文[1] 的基础上设计具有可变抽样区间的单边标准差 (S)图、极差 (R)图和不合格品数 (np)图。计算了这三个图发信号前的平均样本数和平均时间 ,并同固定抽样区间的常规控制图作比较。所设计的控制图能缩短过程失控时间从而减少不合格品数。     

Improving the Performance of the Chi-square Control Chart via Runs Rules

The most popular multivariate process monitoring and control procedure used in the industry is the chi-square control chart. As with most Shewhart-type control charts, the major disadvantage of the chi-square control chart, is that it only uses the information contained in the most recently inspected sample; as a consequence, it is not very efficient in detecting gradual or small shifts in the process mean vector. During the last decades, the performance improvement of the chi-square control chart has attracted continuous research interest. In this paper we introduce a simple modification of the chi-square control chart which makes use of the notion of runs to improve the sensitivity of the chart in the case of small and moderate process mean vector shifts.      

小批量生产时的中位数控制图

设计总体服从正态分布的小批量生产中位数控制图的控制限,首先导出与中位数有关的统计量的分布,然后将中位数标准化,找出标准化中位数的分布和近似正态分布,最后分别基于标准差和极差给出小批量生产中位数控制图作出了讨论,给出了随着样本组数可变的控制界限.     

The total median in statistical quality control

In industry, most of the process observations are assumed to come from a normal population, but usually we merely want to control the process mean value. It is thus sensible to find control statistics, which are ‘robust’ to monitor the process mean, giving the expected rate of false alarms whenever that mean is close to the target value, although not under a normal regime. Simulation studies for a few symmetric and asymmetric distributions allow us to suggest the total median as a robust median estimator. We shall here analyse such a robustness, as well as the robustness of the total median chart comparatively to the sample mean chart, whenever we want to control the mean value of a symmetric underlying parent. Some indication is also provided on the comparative out‐of‐control behaviour of the two charts. Copyright © 2004 John Wiley & Sons, Ltd.     

Chi-square Control Charts with Runs Rules

The Hotelling’s χ² control chart is one of the most widely used multivariate charting procedures for monitoring the vector of means of several quality characteristics. As a Shewhart-type control chart, it incorporates information pertaining to most recently inspected sample and subsequently it is relatively insensitive in quickly detecting small magnitude shifts in the process mean vector. A popular solution suggested to overcome this handicap was the use of runs and scans rules as criteria to declare a process out-of-control. During the last years, the examination of Hotelling’s χ² control charts supplemented with various runs rules has attracted continuous research interest. In the present article we study the performance of the Hotelling’s χ² control chart supplemented with a r-out-of-m runs rule. The new control chart demonstrates an improved performance over other competitive runs rules based control charts.     

一个监控方差的与分布无关的变点模型

文献中绝大部分与分布无关的控制图用于监控过程位置参数,如均值或中位数,而非过程方差.该文开发了一个新的与分布无关的控制图,通过整合一个两样本非参数检验和有效的变点模型.所提出的控制图容易计算,方便应用,并且对于探测过程方差的漂移非常有效.因为它避免了在监控之前的一个很长时间的收集数据的阶段,并且它不需要潜在的过程分布的知识,因此,所提出的控制图在开始阶段或者短程运行情况下特别有用.     

基于产品质量控制的设备维修优化模型

根据产品质量和生产该产品的设备退化状态之间的相关性,设计了周期性设备检测与产品质量控制相结合的设备维修策略。该策略是在对设备进行周期性检测的基础上,利用控制图进行产品质量异常波动的检测,结合对设备退化状态的检测选择设备应采取的维修活动。根据这一设备维修策略,利用更新过程理论和统计过程控制方法,构建了基于产品质量控制的设备维修优化模型,并用遗传算法对其进行求解。通过实例仿真验证了该模型的可行性与有效性。     

Distribution‐free precedence control charts with improved runs‐rules

Distribution‐free (nonparametric) control charts are helpful in applications where we do not have enough information about the underlying distribution. The Shewhart precedence charts is a class of Phase I nonparametric charts for location. One of these charts, called the median precedence chart (Med chart hereafter), uses the median of the test sample as the charting statistic, whereas another chart, called the minimum precedence chart (Min chart hereafter), uses the minimum. In this paper, we first study the comparative performance of the Min and the Med charts, respectively, in terms of their in‐control and out‐of‐control run‐length properties in an extensive simulation study. It is seen that neither chart is best as each has its strength in certain situations. Next, we consider enhancing their performance by adding some supplementary runs‐rules. It is seen that the new charts present very attractive run‐length properties, that is, they outperform their competitors in many situations. A summary and some concluding remarks are given. Copyright © 2016 John Wiley & Sons, Ltd.     

合格品链长控制图

在生产过程中不合格品率(p)很低的情况下,利用两个连续不合格品之间的合格品数(合格品链长)控制图来监督过程不合格品率能克服传统的p图所遇到的困难,为生产者提供产品质量改良的信息。本文研究两种合格品链长(CRL)控制图的制定方法,对这些控制图的效率进行了比较,并说明其实际应用。所研究的CRL控制图特别适用于自动化生产过程的100%检验的质量控制。     

A generally weighted moving average control chart for monitoring the coefficient of variation

This paper proposes a generally weighted moving average control chart with adjusted time-varying control limits for monitoring the coefficient of variation of a normally distributed process variable. This control chart is constructed by combining the generally weighted moving average procedure with a resetting model.The implementation of the proposed chart is presented. Some numerical comparison of the proposed chart with several relevant competing control charts is performed. In general, as demonstrated by extensive simulation results, our chart is clearly more sensitive than other competing procedures for each combination of the in-control target value of the coefficient of variation, the sample size and the shift size. Detection examples are given for two industrial manufacturing processes to introduce the proposed control chart.