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。     

控制图判断准则的显著性检验

徐哲，段晓江．控制图判断准则的显著性检验．数理统计与管理，１９９８，１７（３），４５～４８．利用控制图判断生产过程的状态是一种统计判断，必然犯两种错误。本文在规定“小概率事件”标准的基础上，对休哈特控制图判断准则假设检验的显著性水平进行了分析，并直观解释了各种异常情况的系统性变化特点，最后简述了计算机辅助质量控制图的应用     

非对称极差控制图研究

本文针对现有的国家标准极差控制图的一些问题,在极差的统计性质的基础上,提出了非对称极差控制图的想法,并构造了三种不同的非对称极差控制图,分别给出了报警率达到0.27%时的上、下控制限系数,使得它们的控制限计算非常简便易行.本文给出了这三种非对称极差控制图的含义和各自的侧重点,并将它们与国家标准的极差控制图在对应检验的势函数以及平均运行长度两个方面进行了比较.使用这三种非对称极差控制图可以使真实报警率达到0.27%,远小于现有的国家标准极差控制图的真实报警率.无偏控制图由于具有了"无偏"这一优良性质,成为三张非对称控制图中最具吸引力的.     

合成控制图

本文提出了一种新的组合控制图 ,介绍了它的用法 ,导出了控制限常数 ,并将它和传统的 X图与 X-EWMA 图作了比较。模拟研究表明 ,新图更有效     

自相关过程的MUBM控制图

本文在UBM控制图(Unweighted Batch Means Chart)和Modified Shewhart控制图的基础上提出了适用于过程数据高度自相关且无模型假定的MUBM控制图(Modified Unweighted Batch Means Chart),通过随机模拟发现MUBM控制图比残差控制图(基于残差的Shewhart控制图)更加灵敏,并运用实例数据对MUBM控制图的设计作了说明。     

自相关过程的质量控制图

常规控制图应用的基本假设是从过程得到的测量值彼此独立,但许多连续型的制造业生产过程(例如化学和制药)往往存在自相关,此时常规控制图容易虚发警报。基于数据的样本自相关函数,本文改进了常规控制图的控制界限,使之适用于自相关过程,并运用常规X-s控制图和本文修正的X控制图对一个实际案例进行了比较分析,结果表明本文修正的X控制图可正确地判断过程是否处于受控状态。     

带警戒限的合格品链长控制图

研究了两种带警戒限的合格品链长控制图,一种是一个链长控制图,另一种是两个链长控制图.首先,给出了这两种控制图的制定方法;其次,利用概率流图方法得到了这两种控制图的平均链长(ARL)的计算公式;最后,推导了它们效率的度量指标ANI(发信号之前的平均检验的样品数)的计算公式.     

改进的SPC控制图在食品质量检测中的应用

统计过程控制中的控制图是从事统计过程管理常用的重要工具多年的发展与实践表明传统的控制图已得到广泛的应用可是运用多张控制图进行过程控制仍然存在许多不便和弊端.对此用综合主成分分析法对传统的控制图进行了整合,从而得到一张综合控制图并用这个改进的综合控制图对食品检验过程进行控制,给出了具体的应用步骤以及对结果进行了详细地分析.其结果表明综合控制图不仅结合了传统控制图的优点避免了运用多张控制图进行控制的不便同时又提升了警报的准确率降低了虚假警报的概率.     

自相关过程的改进型EWMA控制图

常规指数加权移动平均(EWMA)控制图的假设前提是观测数据相互独立,但在实际生产过程中,数据相关违背假设条件。本文首先讨论了序列自相关对常规EWMA控制图的影响,结果表明其检测效能降低。因此,重新估计了平稳过程的σz并在此基础上建立了改进型EWMA控制图。然后运用平均链长比较了改进型EWMA控制图与休哈特图和残差控制图,模拟研究说明当过程非强相关且过程均值发生中小偏移条件下。改进型EWMA控制图的检测效果要优于其他两种控制图。最后,通过一个实例验证了该方法的有效性。     

基于回归调整CUSUM控制图的流感监测

流感事件的不可控将对公众健康构成威胁,控制图可以对流感进行监控并对流感爆发进行预警.在实际生活中,每天的流感人数不是同分布的,会受到如温度和湿度等相关因素影响,忽视这些因素可能会使控制图出现误报从而影响疾控部门的决策.考虑到这些因素,文章基于风险调整零膨胀泊松CUSUM控制图提出了一种针对于正常泊松分布的回归调整CUSUM控制图.并且通过蒙特卡洛随机模拟方法算出控制限,分析了文章提出的回归调整CUSUM控制图失控状态下的性能,并与传统CUSUM控制图进行了比较,模拟结果显示回归调整CUSUM控制图明显提高了对漂移的监测效率.最后基于文章提出的方法对香港一家医院的流感人数进行监测,并对流感爆发进行了准确的预警.     

Phase II monitoring of changes in mean from high‐dimensional data

The generalized T² chart (GT‐chart), which is composed of the T² statistic based on a small number of principal components and the remaining components, is a popular alternative to the traditional Hotelling's T² control chart. However, the application of the GT‐chart to high‐dimensional data, which are now ubiquitous, encounters difficulties from high dimensionality similar to other multivariate procedures. The sample principal components and their eigenvalues do not consistently estimate the population values, and the GT‐chart relying on them is also inconsistent in estimating the control limits. In this paper, we investigate the effects of high dimensionality on the GT‐chart and then propose a corrected GT‐chart using the recent results of random matrix theory for the spiked covariance model. We numerically show that the corrected GT‐chart exhibits superior performance compared to the existing methods, including the GT‐chart and Hotelling's T² control chart, under various high‐dimensional cases. Finally, we apply the proposed corrected GT‐chart to monitor chemical processes introduced in the literature.     

Continuity Adjustment for Control Charts for Attributes

A unified approach is proposed for making a continuity adjustment on some control charts for attributes, e.g., np-chart and c-chart. through adding a uniform (0,1) random observation to the conventional sample statistic (e.g., npi and ci). The adjusted sample statistic then has a continuous distribution. Consequently, given any Type I risk a (the probability that the sample statistic is on or beyond the control limits), control charts achieving the exact value of a can be readily constructed. Guidelines are given for when to use the continuity adjustment control chart, the conventional Shewhart control chart (with ±3 standard deviations control limits), and the control chart based on the exact distribution of the sample statistic before adjustment.     

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.     

多元自相关过程的VAR控制图

为了解决多元自相关过程的残差T~2控制图对小偏移不灵敏的问题,本文利用批量-均值法的思想,结合VAR模型的渐近分布,设计了多元自相关过程的向量自回归(VAR)控制图.只要子组样本量足够大,VAR控制图可以对过程出现的各种偏移进行有效控制.通过对比残差T~2控制图的控制效果,得出VAR控制图对小偏移灵敏、残差T~2控制图对大偏移灵敏的结论,联合使用VAR控制图和残差T~2控制图可更有效地监控多元自相关过程。     

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.      

Using one EWMA chart to jointly monitor the process mean and variance

Two control charts are usually used to monitor the process mean and variance separately. The mean is monitored using the [`(X)]{\bar{{X}}} chart while the variance using either the standard deviation, S chart, or the range, R chart. Recently, numerous single variable charts are proposed to jointly monitor the mean and variance. Most approaches transform the sample mean and sample variance into two statistics, each having a standard scale, and either plotting them on the same chart or combining them into a single statistic to be plotted on a chart. The R chart is more widely used than the S chart but no attempt is made to combine the [`(X)]{\bar{{X}}} and R charts in the construction of a single variable chart and to study its properties and performance. In this paper, we transform the [`(X)]{\bar{{X}}} and R statistics into two standard normal random variables, used in the computation of two corresponding exponentially weighted moving average (EWMA) statistics, which are then merged into a single plotting statistic for the proposed chart, called the EWMA [`(X)]-R{\bar{{X}}-R} chart.     

Max control chart with adaptive sample sizes for jointly monitoring process mean and standard deviation

This paper develops the two-state and three-state adaptive sample size control schemes based on the Max chart to simultaneously monitor the process mean and standard deviation. Since the Max chart is a single variables control chart where only one plotting statistic is needed, the design and operation of adaptive sample size schemes for this chart will be simpler than those for the joint X? and S charts. Three types of processes including on-target initial, off-target initial and steady-state conditions are considered to evaluate the chart performance. The results of this study show that both two-state and three-state schemes are more efficient than the conventional non-adaptive joint X? and S charts. The three-state procedure is only slightly better than the two-state scheme, and the most dramatic improvement occurs when the two-state scheme is compared with the non-adaptive joint X? and S charts. Moreover, with the ease of implementation, the two-state scheme is likely adequate in most practical applications.     

基于粒子滤波的改进型单值控制图

针对高度复杂小批量生产环境下的统计过程控制问题,提出基于粒子滤波的改进型单值控制图。通过状态空间模型描述过程运行特征,并运用粒子滤波技术估计过程的运行状态,以状态粒子群的均值为对象,运用平均移动极差控制图对正态分布过程的漂移进行监控。研究结果表明,该方法是小批量生产过程质量控制的有效工具。     

关于累积和(CUSUM)检验的改进

对连续检验问题，常用的检测方法有三大类其一是众所周知的Shewhartt控制图，它是最常用的对生产过程进行连续监控的控制方法，不过，如果过程均值有小的漂移(即μ-μo小)时，Shewhart控制图的检验效果不是很好，除了Shewhart控制图外，另有二类常用的控制图法，其一是累积和控制图(CUSUM)，由Page^[1]基于似然比导出，其二是指数加权移动平均控制图(EWMA)，由Roberts^[2]给出，它们已被证明在检测小的漂移时效果不错。许多人对CUSUM与EWMA进行了比较，总的来说。最好的CUSUM与最好的EWMA在检测小的漂移方面难分优劣，但CUSUM是由似然比导出的，且其平均运行长度的计算相对来说要简便些，因此，CUSUM在与EWMA的比较中更具优势，应用更广．我们分析了CUSUM的导出过程和公式。指出CUSUM有二个可以进一步改进的方面在此基础上，我们给出了二个新的累积和检验统计量及其判断难则，它们分别是PCUSUM检验统计量Pn和DCUSUM检验统计量Sn．在连续检验问题中判断一个检验方法好坏的最重要的标难是其平均运行长度比较标难是在要求具有相同的受控状态下平均运行长度ARL0的条件下，比较其失控状态下的平均运行长度ARL1，ARL1越小越好我们对PCUSUM检验和DCUSUM检验都建立了其平均运行长度ARL的计算公式．通过对CUSUM，PCUSUM，DCUSUM的平均运行长度的比较我们发现我们提出的新的累积和控制方法确比原来的CUSUM有较大改进。     

具有可变抽样区间的非正态累积和控制图

当过程存在小波动时,累积和控制图比传统的休哈特控制图监控效果灵敏。为了提高控制图的监控效率,本文针对非正态情形下的累积和控制图进行可变抽样区间设计。首先用Burr分布近似各种非正态分布,构造可变抽样区间的非正态累积和控制图;其次利用马尓可夫链方法计算其平均报警时间;最后研究结果表明, 所设计的可变抽样区间非正态累积和控制图较固定抽样区间的非正态累积和控制图能更好地监控过程的变化。