Q控制图灵敏性分析

对过程均值未知,过程标准偏差已知情况下的单值Q控制图与均值Q控制图检测过程均值一次偏移的灵敏性进行了分析.结果表明:在过程初始阶段,单值Q图的灵敏性要高于均值Q控制图.在过程初始阶段,建立单值Q控制图监测过程波动,累积一定数量的样本后,建立均值Q控制图监视过程波动.两种Q控制图互为补充,共同用于监视过程波动更有效.     

非共轭先验分布下的贝叶斯统计质量控制研究

贝叶斯质量控制是企业小批量、多品种生产中质量控制的有效手段.在非共轭先验分布的条件下,利用基于马尔科夫链—蒙特卡罗(MCMC)的方法可以解决贝叶斯质量控制中的后验分布确定问题,并用WinBUGS软件执行.在方差未知的正态分布多元质量特性参数控制的模型中,对生产过程中控制图的控制界限进行了预测,在有限的信息条件下,实现对质量特性参数的控制.     

面向质量目标的Q控制图设计

针对大批量生产开始阶段的过程监控,提出了一种基于预定质量目标的Q控制图监控方法.其基本思路是利用面向质量目标的统计公差技术与Q控制图相结合应用,以实现大批量过程开始阶段均值和方差未知时面向质量目标的过程监控.基于质量目标建立统计公差(CP*,k*),并将该统计公差转化为基于给定置信概率的对CP和k的估计值的判定条件.通过案例分析,面向质量目标的Q控制图能够在过程保持受控状态的前提下以一定置信概率保证质量目标.     

脉管制冷机的性能特征

为了研究脉管制冷机的性能特征，本研究课题业已完成了多项实验。结果发现，瞬态或起动期间的冷却时间ｔｃ由脉管壁时间常数τｐｔ所控制，且基本型脉管（ＢＰＴ）制冷机的动态特性可作为一阶系统处理。在稳态运行中，已发现冷端温度ＴＬ随τＰｔ而变化，并且冷负荷ＱＬ随τｐｔ增大而单值增大。这表明，由气体从冷端至热端所泵送的热量随ｈｐｔ的下降而增大（即气体与壁之间的能量交换较小）。从而表明，脉管壁的储热或放热过程对ＢＰＴ制冷机的性能具有消极的作用。还以实验方法发现，脉管内气体的压缩／膨胀过程可以说明ＢＰＴ制冷机的性能，这些过程类似于布雷顿循环，不过介于等温和绝热过程之间。本文实验还表明，脉管制冷机在瞬态和稳态的性能主要地是由脉管壁时间常数τｐｔ，所控制。     

单值移动极差控制图在脱旨浓度控制过程中的应用

本文简要介绍了控制图的作用和种类,通过对脱脂浓度参数一游离碱浓度作单值移动极差控制图进行分析监控,表明单值移动极差控制阻对异常反应灵敏,能够很好地起到预防控制作用。     

单值控制图计算质量特性值标准差的方法

单值控制图计算方法是对国家标准GB/T 28863-2012《商品质量监督抽样检验程序具有先验质量信息的情形》计算质量特性值标准差进行方法补充。     

VSI Q控制图应用研究

VSI Q控制图可以解决经典休哈特控制图在样本数据缺乏的情况下不能应用的问题,并且可以加快检出速度.具体方法是将样本数据转换成服从标准正态分布的统计数据,再利用可变抽样区间技术确定抽样间隔时间.应用研究证明,VSI Q控制图在缺乏样本数据时也能用于监控生产过程,快速检测出异常.     

光合细菌液中动态膜的过滤特性

以旋叶膜滤机作为过滤设备对光合细菌发酵液过滤物料进行动态微孔膜膜过滤特性的研究.研究了操作条件如压力、转速、浓度等对微孔膜过滤中的速率和阻力大小及分布情况的影响,发现整个过滤过程分为两个阶段,过滤初始阶段和稳态过滤阶段.在第二个阶段过滤阻力和过滤时间呈线形变化.     

基于GRU神经网络的自相关过程残差控制图

为提升自相关过程监控的效率,提出基于门控循环单元(gated recurrent unit,GRU)神经网络的自相关过程残差控制图。采用受控下的自相关过程数据对GRU网络进行离线训练与测试,对预测误差进行监控,形成控制用残差控制图。采用训练好的GRU网络预测当前过程波动,利用控制用残差控制图判定当前过程是否失控。运用蒙特卡洛仿真法,与基于一阶自回归模型、BP神经网络以及支持向量回归构建的残差控制图进行性能对比。研究表明,过程受控时,所提残差控制图与其他3种的稳态平均运行链长相差不大,即4者的性能表现相当;而在均值偏移异常过程中,所提残差控制图的平均运行链长远小于其他3种,对自相关过程均值偏移具有较好的监控性能。     

控制图在乳化炸药水相密度控制中的应用

文章介绍了在乳化炸药生产过程中,以水相密度作为质量特性值,应用控制图对其进行统计过程控制.应用表明在生产过程中使用控制图进行监控能够对过程变异起到有效的预防作用.     

A kalman filtering process control scheme with an application in semiconductor short run manufacturing

A quality control chart for monitoring a short run process during the start-up phase is presented in this article. The chart is based on the Kalman filter recursive equations being applied to a stable process where the process variance is unknown prior to the start of the production run. The run length properties of this control scheme are discussed. It is shown that for the proposed scheme the run length properties are independent of the unknown process variance and that these properties are appropriate for monitoring a stable process during start-up. An economic model for the optimal design of the control scheme is presented and illustrated with a wet etching process used in semiconductor manufacturing.     

Cusum of Q chart with variable sampling intervals for monitoring the process mean

Recently, adaptive control charts (that is, with variable sample sizes and/or sampling intervals) for univariate or multivariate quality characteristics have received considerable attention in Phase II analysis in the literature. Due to insufficient samples to obtain good knowledge of the parameters in the start-up process, adding adaptive features to self-starting control charts remains an open problem. In this paper, we propose an adaptive Cusum of Q chart with variable sampling intervals for monitoring the process mean of normally distributed variables. A Fortran program is available to assist in the design of the control chart with different parameters. The effect of the control chart parameters on the performance is studied in detail. The control chart is further enhanced by finding adaptive reference values. Due to the powerful properties of the proposed control chart, the Monte Carlo simulation results show that it provides quite satisfactory performance in various cases. The proposed control chart is applied to a real-life data example to illustrate its implementation.     

The Generally Weighted Moving Average Control Chart for Detecting Small Shifts in the Process Mean

A generalization of the exponentially weighted moving average (EWMA) control chart is proposed and analyzed. The generalized control chart we have proposed is called the generally weighted moving average (GWMA) control chart. The GWMA control chart, with time-varying control limits to detect start-up shifts more sensitively, performs better in detecting small shifts of the process mean. We use simulation to evaluate the average run length (ARL) properties of the EWMA control chart and GWMA control chart. An extensive comparison reveals that the GWMA control chart is more sensitive than the EWMA control chart for detecting small shifts in the mean of a process. To enhance the detection ability of the GWMA control chart, we submit the composite Shewhart-GWMA scheme to monitor process mean. The composite Shewhart-GWMA control chart with/without runs rules is more sensitive than the GWMA control chart in detecting small shifts of the process mean. The resulting ARLs obtained by the GWMA control chart when the assumption of normality is violated are discussed.     

Cumulative conformance count chart with sequentially updated parameters

The Cumulative Conformance Count (CCC) chart has been used for monitoring processes with a low percentage of nonconforming items. However, previous work has not addressed the problem of establishing the chart when the parameter is estimated with a prescribed sampling scheme. This is a prevalent problem in statistical process control where the true values of the process parameters are not known but it is desired to determine if there have been drifts since process start-up. This situation is also not well-covered by the conventional CCC chart, which generally assumes known process parameters. In this paper, we examine a sequential sampling scheme for a CCC chart that arises naturally in practice and investigate the performance of the chart constructed using an unbiased estimator of the percent nonconforming, p. In particular, we examine the false alarm rate and its intended target as well as deriving the mean and standard deviation of the run length; and compare the performance with that established under a binomial sampling scheme. We then propose a scheme for constructing the CCC chart in which the estimated p can be updated and the control limits are revised so that not only the in-control average run length of the chart is always a constant but it is also the largest which is not the case for the CCC chart even when the true p is known. It is shown that the proposed scheme performs well in detecting process changes, even in comparison with the often utopian situation in which the process parameter, p, is known exactly prior to the start of the CCC chart.     

A multivariate EWMA control chart for monitoring process variability with individual observations

A control chart is proposed to effectively monitor changes in the population variance-covariance matrix of a multivariate normal process when individual observations are collected. The proposed control chart is constructed based on first taking the exponentially weighted moving average of the product of each observation and its transpose. Appropriate statistics which are based on square distances between estimators and true parameters are then developed to detect changes in the variances and covariances of the variance-covariance matrix. The simulation studies show that the proposed control chart outperforms existing procedures in cases where either the variances or correlations increase or both increase. The improvement in performance of the proposed control chart is particularly notable when variables are strongly positively correlated. The proposed control chart is applied to a real-life example taken from the semiconductor industry.     

Monitoring multivariate process variability with individual observations via penalised likelihood estimation

Excessive variation in a manufacturing process is one of the major causes of a high defect rate and poor product quality. Therefore, quick detection of changes, especially increases in process variability, is essential for quality control. In modern manufacturing environments, most of the quality characteristics that have to be closely monitored are multivariate by the nature of the applications. In these multivariate settings, the monitoring of process variability is considerably more difficult than monitoring a univariate variance, especially if the manufacturing environment only allows for the collection of individual observations. Some recent charts, such as the MaxMEWMV chart, the MEWMS chart and the MEWMC chart, have been proposed to monitor process variability specifically when the subgroup size is equal to 1. However, these methods do not take into account the engineering and operational understanding of how the process works. That is, when the process variability goes out of control, it is often the case that changes only occur in a small number of elements of the covariance matrix or the precision matrix. In this work, we propose a control charting mechanism that enhances the existing methods via penalised likelihood estimation of the precision matrix when only individual observations are available for monitoring the process variability. The average run length of the proposed chart is compared with that of the MaxMEWMV, MEWMS and MEWMC charts. A real example is also presented in which the proposed chart and the existing charts are applied and compared.     

统计过程控制在微电脑控制器生产中的应用:案例研究

以某电子集团生产的某类型控制器的实际生产为例,采用因果分析法对其生产过程中造成质量缺陷的原因从5M1E(人、机、料、法、测、环)方面进行综合分析.通过对其测量系统进行监控,在其稳定可靠的情况下采集数据.运用SPC技术对波峰焊工序进行控制图监控,对失控原因进行分析并在线调整.有效地保证了控制器的质量,为其质量改善指明了方向.     

A control chart based on a change-point model for monitoring linear profiles

A control chart based on the change-point model is proposed that is able to monitor linear profiles whose parameters are unknown but can be estimated from historical data. This chart can detect a shift in either the intercept, slope or standard deviation. Simulation results show that the proposed approach performs well across a range of possible shifts, and that it can be used during the start-up stages of a process. Simple diagnostic aids are also given to estimate the location of the change and to determine which of the parameters has changed.     

On the monitoring of trended and regularly adjusted processes

Trended and regularly adjusted processes are common in manufacturing industries. Such processes are, for example, related to tool wear, material replenishment or some regular maintenance. When the process has a slow trend or is frequently adjusted, the Shewhart chart can be interpreted in the same way as for a stable process. To facilitate comparison between such a trended and adjusted process to a stable case, and to estimate further the loss of effectiveness when the traditional Shewhart chart is applied to trended and adjusted process, this paper provides a statistical interpretation of traditional Shewhart charts for this type of processes. Formulas are derived for the calculation of alarm rate and average run length (ARL). This study is useful when deciding if a traditional Shewhart chart is sufficient or if a more advanced Statistical Process Control method is necessary. Furthermore, given the in-control and out-of-control ARL, a combined decision with regard to the control limits setting and the adjustment interval can be made. The general formulation is described and a simple linear trend model with an actual data set is used as an illustration.     

Increasing the Sensitivity of Multivariate EWMA Control Chart

A multivariate exponentially weighted moving average (MEWMA) control chart is used for fast detection of small shifts in multivariate statistical quality control. However, for ease of computation, the MEWMA control chart statistics are computed based on the asymptotic form of their covariance matrix in most cases. Another reason that justifies the design of the MEWMA control chart using the asymptotic covariance matrix is that the chart will be insensitive at start-up since processes are more likely to be away from the target value when the control scheme is initiated due to start-up problems. However, if initial out-of-control conditions are deemed important for quick detection, then the MEWMA statistics should be computed based on the exact covariance matrix, as it leads to a natural fast initial response for the MEWMA chart. It will also be shown in this paper the importance of computing the MEWMA statistics based on the exact form of their covariance matrix to further enhance the MEWMA control chart's sensitivity for detecting small shifts. The MEWMA statistics based on the asymptotic and the exact form of their covariance matrix will be referred to as the asymptotic and the exact MEWMA statistics, respectively. Plots and factors that simplify the design of the exact MEWMA control chart are also given.