具有可变抽样区间的Poisson EWMA控制图

传统的EWMA控制图通常都是针对计量型质量特性值的,而对于计数型质量特征值少有研究.设计了单位缺陷数服从Poisson分布的EWMA控制图,并对Poisson EWMA控制图进行了可变抽样区间设计,利用Markov chain方法计算了其平均报警时间,计算结果表明,所设计的动态Poisson EWMA控制图较Shewhart c-图和固定抽样区间的Poissin EWMA控制图能更好的监控过程的变化.     

可变抽样区间同时监控均值标准差的EWMA控制图

传统的EWMA控制图分别针对监控过程均值变化和监控过程标准差变化进行研究,在实际生产中,很多情形需要同时监控过程均值变化和过程标准差变化。为了提高控制图的监控效率,本文研究了同时监控均值和标准差变化时,EWMA控制图的可变抽样区间设计。其次运用马科夫链法计算可变抽样区间EWMA控制图的平均报警时间;然后与传统的EWMA控制图进行比较得出:同时监控均值和标准差时,可变抽样区间的EWMA控制图能够更快地发现过程中的异常波动,具有较短的平均报警时间,其监控效率明显优于传统的EWMA控制图。     

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。     

具有可变样本容量的非正态EWMA控制图

为了提高控制图的监控效率,本文研究非正态分布下,EWMA控制图的可变样本容量设计问题。首先利用Burr分布近似各种非正态分布,构造可变样本容量的非正态EWMA控制图;其次运用马尓科夫链法计算可变样本容量非正态EWMA控制图的平均运行长度;然后与传统的非正态EWMA控制图进行比较得出:当过程中出现小波动时,可变样本容量的非正态EWMA控制图能够更快地发现过程中的异常波动,具有较小的平均运行长度,其监控效率明显优于传统的非正态EWMA控制图。     

可变抽样区间的二项EWMA控制图

在制造过程中,对产品的不合格品数进行监控时,通常选用计数性控制图-np图,它是基于过程服从二项分布建立的,一般对于过程中出现的较大波动效果明显。为了提高控制图对不合格品数较小波动的监控效果,本文设计了产品不合格品数服从二项分布的EWMA控制图。提出可变抽样区间的二项EWMA控制图,并采用马可夫链法计算其平均报警时间。对固定抽样区间以及可变抽样区间二项EWMA控制图对比研究,表明当过程失控时,可变抽样区间二项EWMA控制图具有较小的失控平均报警时间,能够迅速监测出过程中的异常波动,明显优于固定抽样区间的二项EWMA控制图。     

自相关对常规控制图影响的模拟研究与案例分析

常规统计控制图的基本假设前提是观测值独立同分布,而在实际生产过程中,质量指标值常表现出自相关现象,违背独立性假定。本文运用平均链长(ARL)研究自相关过程为AR(1)时对常规控制图的影响,并比较了常规控制图和残差控制图对序列相关过程的控制效果。模拟结果和实例分析表明:当过程序列相关时,使用常规作图法估计出的标准差是有偏的,致使控制限设置错误和常规控制图检测能力降低。因此,在一些统计过程控制中,须考虑自相关现象并采用适当的控制图方法。     

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。     

基于条件表现的EWMA-Maxwell控制图精确设计

多数控制图设计均基于过程服从正态分布,且受控过程参数已知的假定,然而实际中,过程真实分布常常偏态,且过程参数也经常未知。本文首先设计了等尾和平均链长无偏(无偏)EWMA图用于偏态Maxwell分布的监控,接着从条件视角出发推导出了条件平均链长的精确分布,讨论了参数估计对所提控制图的影响,并基于超越概率准则给出为将参数估计所引致的伪警报率降低到合理水平所需的第I阶段最小样本数。为弥补实际应用中第I阶段样本数据的不足,提出精确修正方法设计具有理想条件表现的等尾和无偏EWMA图用于早期过程的监控。结果表明,相较于未修正图,修正图发现过程失控的能力略有下降。另外,修正无偏图整体上优于等尾图。最后通过实例说明所设计控制图的实际执行。     

阶段Ⅱ联合检测过程位置和尺度的非参数EWMA控制图（英文）

本文提出了一个基于Cucconi检验的非参数指数加权移动平均(EWMA)控制图(简称为EC图)来同时检测过程位置参数和尺度参数.依据步长分布的均值、方差及分位数,给出了EC图与其他一些现有的非参数EWMA控制图的模拟比较.基于蒙特卡洛的模拟结果表明,EC图具有很好的性能.详细分析了阶段I中参考样本大小对EC图受控性能的影响.最后用一个实例来说明EC图的实际应用。     

残差EWMA-图对平稳自回归过程数据的检验能力

本文将SPC(Statistical Process Control)技术应用于自相关数据,使用的基本方法是对数据做残差处理,本文定义了一个衡量检验能力的指标。并给出了对于平稳自回归过程数据,在检验过程均值变化方面,残差EWMA—图检验能力强弱的条件。     

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??A single distribution-free (nonparametric) Phase II exponentially weighted moving average (EWMA) chart based on the Cucconi statistic, referred to as the EWMA-Cucconi (EC) chart, is considered here for simultaneously monitoring shifts in the unknown location and scale parameters of a univariate continuous process. A comparison with some other existing nonparametric EWMA charts is presented in terms of the average, the standard deviation and some percentiles of the run length distribution. Numerical results based on Monte Carlo analysis show that the EC chart provides quite a satisfactory performance. The effect of the Phase I (reference) sample size on the IC performance of the EC chart is studied in detail. The application of the EC chart is illustrated by two real data examples.     

A Distribution-Free Phase II EWMA Control Chart for Joint Monitoring of Location and Scale

A single distribution-free (nonparametric) Phase II exponentially weighted moving average (EWMA) chart based on the Cucconi statistic, referred to as the EWMA-Cucconi (EC) chart, is considered here for simultaneously monitoring shifts in the unknown location and scale parameters of a univariate continuous process. A comparison with some other existing nonparametric EWMA charts is presented in terms of the average, the standard deviation and some percentiles of the run length distribution. Numerical results based on Monte Carlo analysis show that the EC chart provides quite a satisfactory performance. The effect of the Phase I (reference) sample size on the IC performance of the EC chart is studied in detail. The application of the EC chart is illustrated by two real data examples.     

Individuals control schemes for monitoring the mean and variance of processes subject to drifts

This paper investigates control chart schemes for detecting drifts in the process mean μ and/or process standard deviation σ when individual observations are sampled. Drifts may be due to causes such as gradual deterioration of equipment, catalyst aging, waste accumulation, or human causes, such as operator fatigue or close supervision. The standard Shewhart X chart and moving range (MR) chart are evaluated, as well as several types of exponentially weighted moving average (EWMA) charts and combinations of charts involving these EWMA charts. We show that the combinations of the EWMA charts detect slow-rate and moderate-rate drifts much faster than the combined X and MR charts. We also show that varying the sampling interval adaptively as a function of the process data results in notable reductions in the detection delay of drifts in μ and/or σ.     

Nonparametric Multivariate CUSUM Chart and EWMA Chart Based on Simplicial Data Depth

Simplicial data depth is a useful tool for describing how central a vector is in a multivariate distribution. If the average simplicial depth of a subgroup of observations from a multivariate distribution is too small, it may indicate that a shift in its location or/both scale occurs. In this paper, we propose two new types of nonparametric control charts which are one-sided CUSUM and EWMA control schemes based on simplicial data depth. We also compute the Average Run Length of the CUSUM chart and the EWMA chart by Markov chain method. Recommendations on how to choose the optimal reference value and the smoothing parameter are also given. Comparisons between these two proposed control schemes and the multivariate EWMA are presented.     

Joint economic design of EWMA control charts for mean and variance

Control charts with exponentially weighted moving average (EWMA) statistics (mean and variance) are used to jointly monitor the mean and variance of a process. An EWMA cost minimization model is presented to design the joint control scheme based on pure economic or both economic and statistical performance criteria. The pure economic model is extended to the economic-statistical design by adding constraints associated with in-control and out-of-control average run lengths. The quality related production costs are calculated using Taguchi’s quadratic loss function. The optimal values of smoothing constants, sampling interval, sample size, and control chart limits are determined by using a numerical search method. The average run length of the control scheme is computed by using the Markov chain approach. Computational study indicates that optimal sample sizes decrease as the magnitudes of shifts in mean and/or variance increase, and higher values of quality loss coefficient lead to shorter sampling intervals. The sensitivity analysis results regarding the effects of various inputs on the chart parameters provide useful guidelines for designing an EWMA-based process control scheme when there exists an assignable cause generating concurrent changes in process mean and variance.     

可变抽样区间几何EWMA控制图的经济设计

为了降低生产过程周期成本,本文对单位缺陷数服从几何分布时,可变抽样区间的指数加权移动平均(EWMA)控制图进行经济设计。首先建立可变抽样区间几何EWMA控制图的经济模型,使单位时间期望费用最小来确定参数的最优值;其次用遗传算法来寻找经济模型的最优解;最后对可变抽样区间几何EWMA控制图的经济模型进行灵敏度分析和最优性分析。研究结果表明单位时间期望费用分别随着异常原因发生的频率、过程失控时单位时间的质量费用、发现异常原因的时间期望值和纠正过程的时间期望值的增大而增大。