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。     

基于可变抽样区间的累积和方差控制图研究

介绍了基于对数方差的累积和控制图,进行了可变抽样区间的控制图设计用Markov链方法计算可变抽样区间的累积和方差控制图的平均报警时间,并且与固定抽样区间的控制图进行比较,分析在不同参数取值下的平均报警时间.     

可变抽样区间的多变量自相关过程VAR控制图

基于批量-均值法的思想,向量自回归(VAR)控制图对多变量自相关过程的较小偏移可以进行有效控制。为了提高多变量自相关过程监控效率,本文研究可变抽样区间的VAR控制图。首先,对多变量自相关过程的VAR控制图进行可变抽样区间设计;然后,用蒙特卡洛模拟方法计算其平均报警时间;最后,以平均报警时间为评价准则,对所设计的可变抽样区间VAR控制图与固定抽样区间的VAR控制图进行比较研究。研究结果表明:所设计的可变抽样区间多变量自相关过程VAR控制图较固定抽样区间的多变量自相关过程VAR控制图能更好的监控过程的变化。     

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。     

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.     

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.     

可变抽样区间图经济设计分析

传统控制图是静态的,其抽样区间为固定常数,往往不能及时发现生产过程异常。针对这种情况,本文在综合抽样成本、生产次品损失、误报警和漏报警损失、发现并纠正过程异常成本等基础上,提出可变抽样区间X图的经济设计方法。根据过程实际状态和由抽样结果采取的决策构建了一个二维时间离散的马尔可夫链,提出优化模型,并利用遗传算法寻找控制图参数的最优解。数值计算给出本文模型的具体求解过程。灵敏度分析研究了各参数对最优样本容量、控制限、警戒限、抽样区间及单位时间平均成本的影响。     

可变抽样区间的非参数控制图

最近几年一些学者研究了可变抽样区间的质量控制图。Amin等提出了可变抽样区间(VSI)的非参数控制图———符号 (Sign)统计量图〔1〕。本文在此基础上研究位置VSI符号控制图的制定方法 ,并设计离散VSI符号控制图。符号控制图的优点是对非正态总体亦可应用 ,并且不需要过程方差的信息。本文将所设计的VSI符号控制图同固定抽样区间 (FSI)的常规图作比较 ,并举实例说明符号控制图的应用     

在固定时间抽样的可变抽样区间的极值控制图

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 ]的模型设计极值 (ζ)图。规定样本在相等间隔的固定时间点抽取 ,当过程有变化的迹象时 ,允许在两个固定时间之间抽取附加样本。若样本点超过控制限 ,则 VSIFT图同常规图一样发信号。本文计算了 VSIFTζ图的发信号前的平均时间 ,并同固定抽样区间 (FSI)的常规ζ图作比较。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。本文设计的 VSIFTζ图能缩短过程失控时间从而减少不合格品数     

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.     

An extension of Banerjee and Rahim's model for economic design of moving average control chart for a continuous flow process

In this paper, we propose a model of a moving average control chart (MA control chart) with a Weibull failure mechanism from an economic viewpoint. When the process-failure mechanism follows a Weibull model or other models having increasing hazard rates, it is desirable to have the decreasing sampling interval with the age of the system. The MA control chart is used to monitor quality characteristics of raw material or products in a continuous process. A cost model utilizing a variable scheme instead of fixed sampling lengths in a continuous flow process is studied in this research. The variable sampling scheme is used to maintain a constant integrated hazard rate over each sampling interval. Optimal values for the design parameter, the moving subgroup size, the sampling interval, and the control limit coefficient are determined by minimizing the loss-cost model. The performance of the loss cost with various Weibull parameters is studied. A sensitivity analysis shows that the design parameters and loss cost depend on the model parameters and shift amounts.     

Cumulative conformance count chart with variable sampling intervals and control limits

The cumulative conformance count (CCC) chart has been used for monitoring processes with very low fraction of nonconforming items. Typically, the items produced from the process were examined using 100% inspection for generating the CCC chart. However, this would be costly when taking the inspection cost and time into consideration and thus limit its application. Instead of inspecting the items one by one, this study takes sample from them, and regards the time between two successive samples as the sampling interval. In order to increase the sensitivity of the CCC chart to process change, the sampling interval and control limits are allowed to vary in this study. The average time to signal process change of the modified CCC chart (called the variable sampling interval and control limit (VSI/VCL) CCC chart) is derived by the Markov chain approach and taken as the performance measure to evaluate its statistical efficiency. With some minor changes, this chart can be reduced to the VSI CCC chart, the VCL CCC chart, and the standard CCC chart. In addition, comparisons among them are made and discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

Multivariate nonparametric control chart with variable sampling interval

This paper proposes an adaptive, multivariate, nonparametric, exponentially weighted moving average control chart with variable sampling interval. A number of studies have discussed multivariate nonparametric control charts. However, the proposed multivariate nonparametric control charts usually have strict requirements. In this paper, we construct a control chart for multivariate processes that is based on the Mahalanobis depth. Specifically, we use the concept of the Mahalanobis depth to reduce each multivariate measurement to a univariate index. It is worth mentioning that this approach is completely nonparametric. We also discuss the optimal strategy for the parameters. This chart is an adaptive chart and has a variable sampling interval. A simulation study demonstrates that the proposed chart is efficient in detecting various magnitudes of shifts. A gravel data and a wine quality detection example are given to introduce the proposed control chart.     

可变抽样区间和样本容量的非正态EWMA控制图经济设计

为了降低过程控制成本和提高监控效率,针对质量特性值不服从正态分布的情况,研究可变抽样区间和样本容量(VSSI)的指数加权移动平均(EWMA)控制图的经济设计问题。首先对监控非正态分布的EWMA控制图进行抽样区间和样本容量变化设计;其次建立VSSI 非正态EWMA控制图的经济模型,通过使费用成本函数最小得到控制图的最优设计参数组合;然后给出工业中的一个例子,用遗传算法对经济模型搜寻最优解;接下来对经济模型进行灵敏度分析,得出控制图费用参数与设计参数之间的影响关系;最后通过最优性分析,得出所建立的VSSI 非正态EWMA控制图的经济性优于VSI、VSS非正态EWMA控制图。