可变抽样区间的非正态EWMA图的经济设计

本文主要讨论非正态总体下,可变抽样区间的EWMA图的经济设计问题。首先利用Burr分布近似各种非正态分布,建立可变抽样区间的非正态EWMA图的经济模型,使总期望费用最型小来确定参数的最优值;其次用遗传算法来寻找该经济模型的最优解,并给出工业中的一个例子;最后对可变抽样区间的非正态EWMA图的经济模型进行灵敏度分析。     

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。     

基于预防维修和质量损失函数的VSI EWMA控制图经济设计

为了提高过程监控效率的同时降低过程控制成本,研究可变抽样区间(VSI)指数加权移动平均(EWMA)控制图的经济设计问题。首先建立基于预防维修和质量损失函数的VSI EWMA控制图联合经济模型;使单位时间的损失成本函数最小来确定参数的最优值;其次用遗传算法来寻找联合经济模型的最优解,并给出一个算例。最后对VSI EWMA控制图联合经济模型进行灵敏度分析,得出控制图模型参数对设计参数的影响关系。     

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。     

基于预防维修的可变抽样区间泊松EWMA控制图经济设计

现代质量控制的核心技术和方法始终围绕着如何提高监控效率和降低控制成本问题而展开。统计过程控制是一种常用的质量控制方法，其重要的工具——控制图对过程中的质量特性值进行统计并描点达到监控过程质量状态的目的。控制图的设计方法将会对产品过程的监控效率和控制成本产生较大影响。针对只考虑传统控制图参数固定以及控制图统计设计的情形，传统的缺陷数c控制图基于泊松分布建立以监控制造过程中产品的缺陷数。为了降低控制过程成本并提高缺陷数c控制图的监控效率，研究预防维修策略下可变抽样区间(VSI) EWMA图的经济设计问题。首先，建立泊松分布下VSI EWMA控制图的经济模型。其次，令单位时间的损失成本函数最小得到经济模型的最优解。以化工建材行业为例，说明如何根据预防维修策略下所构建的控制图经济模型得到最优参数。然后，为得到设计参数与模型参数的影响关系，对经济模型灵敏度分析。最后，对经济模型进行最优性分析验证其优越性。     

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。     

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。     

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。     

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.     

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.     

Hotelling’s T charts with variable sample size and control limit

The ideas of variable sampling interval (VSI), variable sample size (VSS), variable sample size and sampling interval (VSSI), and variable parameters (VP) in the univariate case have been successfully applied to the multivariate case to improve the efficiency of Hotelling’s T² chart with fixed sampling rate (FSR) in detecting small process shifts. However, the main disadvantage in using most of these control schemes is an increasing in the complexity due to the adaptive changes in sampling intervals. In this paper, retaining the lengths of sampling intervals constant, a variable sample size and control limit (VSSC) T² chart is proposed and described. The statistical efficiency of the VSSC T² chart in terms of the average time to signal a shift in process mean vector is compared with that of the VP, VSSI, VSS, VSI, and FSR T² charts. From the results of comparison, it shows that the VSSC T² chart for a (very) small shift in the process mean vector gives a better performance than the VSSI, VSS, VSI, and FSR T² charts; meanwhile, it presents a similar performance to the VP T² chart. Furthermore, from the viewpoint of practicability, it is more convenient for administrating the control chart than the VSI, VSSI, and VP T² chart. Thus, it may provide a good option for quick response to small shifts in a multivariate process.     

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.     

Economic design of an attribute control chart for monitoring mean life based on multiple deferred state sampling

In this paper, we design an attribute np control chart using multiple deferred state (MDS) sampling under Weibull distribution based on time truncated life test. This chart is constructed for monitoring the variation of mean life of the product in a manufacturing process. The optimal parameters of MDS sampling and the control limit coefficients are determined so that the in‐control average run length (ARL) is as close as to the target ARL. The optimal parameters of MDS sampling are sample size and number of successive subgroups required for declaring the current state of process. Out‐of‐control ARL is considered as a measure of the performance of proposed chart and reported with determined optimal parameters for various shift constants. The out‐of‐control ARL of the proposed chart obtained under various distributions is compared with each other. The performance of proposed control chart is compared with the performance of the existing control chart designed under single sampling. In addition, the economic design of proposed chart using variable sampling interval scheme is discussed, and sensitivity analysis on expected costs is also investigated.     

Economic design of attribute np control charts using a variable sampling policy

To use a control chart, the quality engineer should specify three decision variables, namely the sample size, the sampling interval and the critical region of the chart. A significant part of recent research relaxed the constraint of using fixed design parameters to open the way to a new type of control charts called adaptive ones where at least one of the decision variables may change in real time based on the last data information. These adaptive schemes have proven their effectiveness from economical and statistical point of views. In this paper, the economic design of an attribute np control chart using a variable sampling interval (VSI) is treated. A sensitivity analysis is conducted to search for optimal design parameters minimizing the expected total cost per hour and to reveal the impact of the process and cost parameters on the behavior of optimal solutions. An economic comparison between the classical np chart, variable sample size (VSS) np control chart and VSI chart is conducted. It is found that switching from the classical attribute chart to the VSI sampling strategy results in notable cost savings and in reduction of the average time to signal and the average number of false alarms. In most cases of the sensitivity analysis, the VSI np chart outperforms the VSS np chart based on economical and statistical considerations. Copyright © 2014 John Wiley & Sons, Ltd.