Optimum Multiple and Multivariate Poisson Statistical Control Charts

This paper deals with the simultaneous statistical process control of several Poisson variables. The practitioner of this type of monitoring may employ a multiple scheme, i.e. one chart for controlling each variable, or may use a multivariate scheme, based on monitoring all the variables with a single control chart. If the user employs the multivariate schemes, he or she can choose from, for example, three options: (i) a control chart based on the sum of the different Poisson variables; (ii) a control chart on the maximum value of the different Poisson variables; and (iii) in the case of only two variables, a chart that monitors the difference between them. In this paper, the previous control charts are studied when applied to the control of p = 2, 3 and 4 variables. In addition, the optimization of a set of univariate Poisson control charts (multiple scheme) is studied. The main purpose of this paper is to help the practitioner to select the most adequate scheme for her/his production process. Towards this goal, a friendly Windows© computer program has been developed. The program returns the best control limits for each control chart and makes a complete comparison of performance among all the previous schemes. Copyright © 2013 John Wiley & Sons, Ltd.     

Multivariate Profile Charts for Statistical Process Control

The multivariate profile (MP) chart is a new control chart for simultaneous display of univariate and multivariate statistics. It is designed to analyze and display extended structures of statistical process control data for various cases of grouping, reference distribution, and use of nominal specifications. For each group of observations, the scaled deviations from reference values are portrayed together as a modified profile plot symbol. The vertical location of the symbol is determined by the multivariate distance of the vector of means from the reference values. The graphical display in the MP chart enjoys improved visual characteristics as compared with previously suggested methods. Moreover, the perceptual tasks required by the use of the MP chart provide higher accuracy in retrieving the quantitative information. This graphical display is used to display other combined univariate and multivariate statistics, such as measures of dispersion, principal components, and cumulative sums     

简化的测量过程统计控制方法

休哈特控制图平均值控制界限的设置对于计量标准或测量设备来说有很大的局限性,根据计量标准或测量设备的特点,提出简化的测量过程统计控制方法,该方法省去了标准偏差或极差控制图。根据标准偏差试验结果的大小,提出了子组最少测量次数的确定方法,平均值控制界限以各组平均值算得的标准偏差来设置,平均值控制图异常的判断准则简化为3种判据,进而大大减少了实施统计控制的工作量,便于推广应用。     

多品种小批量生产的SPC应用研究

综述了多品种小批量生产方式的特点以及该生产方式下SPC(统计过程控制)的应用现状,指出常规控制图不适用于多品种小批量生产的问题,提出结合通用控制图、累积和(Cumulative-Sum,CUSUM)控制图和指数加权移动平均(Exponentially Weighted Moving-Averages,EWMA)控制图的解决方法,并总结出该SPC方法用于处理多品种小批量生产中微小偏差的步骤。     

FMS统计过程控制技术的研究

对统计过程控制技术在柔性控制系统中的应用进行了研究，主要研究内容包括：适用于多品种、小批量生产的质量控制图；基于神经网络的质量控制图异常模式自动识别技术。     

Modeling and Monitoring Ecological Systems—A Statistical Process Control Approach

Statistical process control monitoring of nonlinear relationships (profiles) has been the subject of much research recently. While attention is primarily given to the statistical aspects of the monitoring techniques, little effort has been devoted to developing a general modeling approach that would introduce ‘uniformity of practice’ in modeling nonlinear profiles (analogously with the three‐sigma limits of Shewhart control charts). In this article, we use response modeling methodology (RMM) to demonstrate implementation of this approach to statistical process control monitoring of ecological relationships. Using 10 ecological models that have appeared in the literature, it is first shown that RMM models can replace (approximate) current ecological models with negligible loss in accuracy. Computer simulation is then used to demonstrate that estimated RMM models and estimated data generating ecological models achieve goodness‐of‐fit that is practically indistinguishable from one another. A regression‐adjusted control scheme, based on control charts for the predicted median and for residuals variation, is developed and demonstrated for three types of ‘out of control’ scenarios. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

面向半导体制造业的统计过程分析与控制

传统的统计过程控制方法不能完全适应半导体制造业生产形式需要。本文在分析半导体光电封装制造模式的特点和实施过程控制所面临的问题的基础上,提出一种基于聚类分析的统计质量控制方法。通过实证分析,证实了该方法的可操作性并取得了良好的实际效果。     

On Model Selection for Autocorrelated Processes in Statistical Process Control

Using traditional control charts to monitor autocorrelated processes is not beneficial, because it will lead us to misleading detections in the processes. One of the methods used to deal with the control charts for autocorrelated process is the model‐based approach. It uses an adequate time series model that fits the process and uses the residuals as monitoring statistics. For the said purpose, it is important to pick a suitable model that can adequately be used for different designs of control charts under specific time series model. This study intends to do the same for three popular types of charts namely Shewhart, exponentially weighted moving average, and cumulative sum. The models covered in this study include AR(1), MA(1), and ARMA(1,1) as the potential models to fit the process of interest. We have focused on two performance aspects namely efficiency and robustness. Average run length is used as a performance measure for different in‐control and out‐of‐control states of the autocorrelated processes under varying levels of autocorrelation. An application example based on a real data set is also included in the study to highlight the importance of the study proposals. Copyright © 2016 John Wiley & Sons, Ltd.     

基于Meta分析思想的统计过程质量控制

通过对统计过程质量控制的现状及存在问题的分析,提出基于Meta分析思想进行统计过程质量控制的观点.重点讨论了Meta分析思想的优越性,分析了在统计过程质量控制中引入Meta分析思想的可行性和重要性,并示例演示其流程及意义,最后探讨了该思想在实际应用中存在的问题及可能的解决途径.     

Latent Structures-Based Multivariate Statistical Process Control: A Paradigm Shift

ABSTRACT

The basic fundamentals of statistical process control (SPC) were proposed by Walter Shewhart for data-starved production environments typical in the 1920s and 1930s. In the 21st century, the traditional scarcity of data has given way to a data-rich environment typical of highly automated and computerized modern processes. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio, multistage and multiway structures, and missing values. Conventional univariate and multivariate SPC techniques are not suitable in these environments. This article discusses the paradigm shift to which those working in the quality improvement field should pay keen attention. We advocate the use of latent structure–based multivariate statistical process control methods as efficient quality improvement tools in these massive data contexts. This is a strategic issue for industrial success in the tremendously competitive global market.     

Applying Real-Time Statistical Process Control to Manufacturing Processes Exhibiting Between and Within Part Size Variability in the Wood Products Industry

In this paper, we develop a control charting procedure to monitor average part size, as well as between and within part size variation for sawn lumber in an automated lumbermill. We present a review of the sawing process followed by a discussion of sampling methods for a real-time noncontact laser measuring device. A statistical model based on the components of variation analysis of variance model is proposed both for the analysis of the data and the construction of control charts that can be used to monitor the process. The details of the resulting statistical process control system are developed and an example from the lumber industry is provided and compared to other possible approaches. The resulting techniques may have applicability in many other industries where within and between variation in processes occurs.     

Delay in Statistical Control of Systems with Wear

The target of statistical process control is to identify changes in the behavior of controlled process as quickly as possible. Therefore, as a quality measure of control charts, we use characteristics which quantify the delay between the occurrence of change and its identification by the control chart. The average run length is a commonly used characteristic which does not reflect a real situation. A new characteristic is suggested which is computed in the case of progressive wearing out of the system. We assume several types of progression. The Markov chain approach is used for computation of average delay. Copyright © 2011 John Wiley & Sons, Ltd.     

Bayesian Statistical Process Control

This article presents a general Bayesian statistical process control chart. Most previous applications of Bayes' theorem to quality control have either been tied to a rigid optimization model or have used Bayes' theorem to infer the values of structural parameters of the monitored process. The methodology presented differs from both of these approaches. The result is a flexible tool that can be manipulated by decision makers, as is the case with other types of control charts. The Bayesian chart is demonstrated for joint monitoring of the mean and standard deviation of a normal random variable, and compared to both Shewhart and cumulative sum monitoring. The basis for comparison is the expected number of false alarms per expected time in control and the average out-of-control run length. The comparison identifies types of production process where the Bayesian chart has better expected performance than the other two charts and also shows that even though the Bayesian chart requires more detailed knowledge of process structure, acquiring this knowledge can yield real benefits. The article concludes with a practical example.     

Multiobjective Genetic Algorithm Approach to the Economic Statistical Design of Control Charts with an Application to bar and S2 Charts

Control charts are the primary tools of statistical process control. These charts may be designed by using a simple rule suggested by Shewhart, a statistical criterion, an economic criterion, or a joint economic statistical criterion. Each method has its strengths and weaknesses. One weakness of the methods of design listed is their lack of flexibility and adaptability, a primary objective of practical mathematical models. In this article, we explore multiobjective models as an alternative for the methods listed. These provide a set of optimal solutions rather than a single optimal solution and thus allow the user to tailor their solution to the temporal imperative of a specific industrial situation. We present a solution to a well‐known industrial problem and compare optimal multiobjective designs with economic designs, statistical designs, economic statistical designs, and heuristic designs. Copyright © 2012 John Wiley & Sons, Ltd.     

CUSUM Statistical Monitoring of M/M/1 Queues and Extensions

Many production and service systems can be modeled as queueing systems. Their operational efficiency and performance are often measured using queueing performance metrics (QPMs), such as average cycle time, average waiting length, and throughput rate. These metrics need to be quantitatively evaluated and monitored in real time to continuously improve the system performance. However, QPMs are often highly stochastic, and hence are difficult to monitor using existing methods. In this article, we propose the cumulative sum (CUSUM) schemes to efficiently monitor the performance of typical queueing systems based on different sampling schemes. We use M/M/1 queues to illustrate how to design the CUSUM chart and compare their performance with several alternative methods. We demonstrate that the performance of CUSUM is superior, responding faster to many shift patterns through extensive numerical studies. We also briefly discuss the extensions of CUSUM charts to more general queues, such as M/G/1, G/G/1, or M/M/c queues. We use case studies to demonstrate the applications of our approach. Supplementary materials for this article are available online.     

Context-Based Statistical Process Control

Most statistical process control (SPC) methods are not suitable for monitoring nonlinear and state-dependent processes. This article introduces the context-based SPC (CSPC) methodology for state-dependent data generated by a finite-memory source. The key idea of the CSPC is to monitor the statistical attributes of a process by comparing two context trees at any monitoring period of time. The first is a reference tree that represents the “in control” reference behavior of the process; the second is a monitored tree, generated periodically from a sample of sequenced observations, that represents the behavior of the process at that period. The Kullback–Leibler (KL) statistic is used to measure the relative “distance” between these two trees, and an analytic distribution of this statistic is derived. Monitoring the KL statistic indicates whether there has been any significant change in the process that requires intervention. An example of buffer-level monitoring in a production system demonstrates the viability of the new method with respect to conventional methods.     

基于多点报警准则的高质量过程缺陷率控制图

为了提高用于高质量过程监控的累积计点值控制图的监控效率,提出了一类采用多点报警准则的高质量过程缺陷率控制图设计,并给出了计算这类改进型控制图的平均运行长度的马尔科夫链方法。为了验证该设计方法的改进效果,分析比较了采用多点报警规则的3种累积计点值控制图（报警规则为连续两点出界、连续三点中有两点出界、连续三点出界）与报警规则不改变的（报警规则为有一个点出界）累积计点值控制图的监控效率。比较结果表明：在受控状态下平均运行长度都为370的情况下,多点报警的累积计点值控制图的失控状态下的平均运行长度分别较报警规则不变的控制图的平均运行长度减小了约44%、40%、63%,控制图发现缺陷率增大的效率明显改善。得出结论：这种多点报警的累积计点值控制图发现缺陷率增大的速度提高40%~60%。     

Comparison of Multivariate Control Charts for Process Dispersion

In this article we compare four multivariate control charts for process dispersion in the retrospective analysis of a historical data set. Among the schemes compared, a new control chart based on a robust estimation of the variance-covariance matrix proved to be very effective in detecting changes in the process dispersion matrix.