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.     

基于小批量多品种生产环境的统计过程质量控制研究

分析了先进制造技术环境下实施统计过程质量控制所存在的问题，给出了一种基于正态过程的改进的标准化控制图，适用于小批量多品种生产环境下对过程均值、过程方差的有效控制。     

Statistical Control of a Six Sigma Process

Six Sigma as a methodology for quality improvement is often presented and deployed in terms of the dpmo metric, i.e., defects per million opportunities. As the sigma level of a process improves beyond three, practical interpretation problems could arise when conventional Shewhart control charts are applied during the Control phase of the define-measure-analyze-improve-control framework. In this article, some alternative techniques are described for the monitoring and control of a process that has been successfully improved; the techniques are particularly useful to Six Sigma Black Belts in dealing with high-quality processes. The approach used would thus ensure a smooth transition from a low-sigma process management to maintenance of a high-sigma performance in the closing phase of a Six Sigma project.     

Using Factor Effects Analysis to Improve Statistical Process Control

Quality practitioners are taught that when a control chart shows an out-of-control condition they should take corrective action but not what action. This paper fills a gap because it provides a method of identifying the significant contributors thus providing a guide as to what to do.     

Algorithmic Statistical Process Control: Concepts and an Application

The goal of algorithmic statistical process control is to reduce predictable quality variations using feedback and feedforward techniques and then monitor the complete system to detect and remove unexpected root causes of variation. This methodology seeks to exploit the strengths of both automatic control and statistical process control (SPC), two fields that have developed in relative isolation from one another. Recent experience with the control and monitoring of intrinsic viscosity from a particular General Electric polymerization process has led to a better understanding of how SPC and feedback control can be united into a single system. Building on past work by MacGregor, Box, Astrom, and others, the article covers the application from statistical identification and modeling to implementing feedback control and final SPC monitoring. Operational and technical issues that arose are examined, and a general approach is outlined.     

面向质量目标的零件批统计质量指标的分析与研究

为了有效解决多种质量指标与统计公差、质量控制统计参数间的并行设计问题,以统计公差技术理论为基础,应用统计过程控制技术研究并建立面向质量目标的零件批统计质量指标,通过计算机仿真及实测对统计质量指标进行了试验验证和分析,结果表明了统计质量指标建立的理论方法正确性及应用的可行性。     

Using Statistical Thinking and Designed Experiments to Understand Process Operation

Using a series of four case studies, this article illustrates the integration of statistical process control and designed experiments. For such an integration to be effective, this article points out the need to use statistical process control (SPC) as a tool for active process study, rather than simply as a method for maintaining and controlling processes. The use of SPC in this fashion is also illustrated throughout the case studies.     

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.     

A Statistical Control Chart for Stationary Process Data

In the statistical process control environment, a primary method to deal with autocorrelated data is the use of a residual chart. Although this methodology has the advantage that it can be applied to any autocorrelated data, it needs some modeling effort in practice. In addition, the detection capability of the residual chart is not always great. This article proposes a statistical control chart for stationary process data. It is simple to implement, and no modeling effort is required. Comparisons are made among the proposed chart, the residual chart, and other charts. When the process autocorrelation is not very strong and the mean changes are not large, the new chart performs better than the residual chart and the other charts.     

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。     

面向小批量生产的统计过程控制的研究

指出了在小批量生产环境下实施统计过程控制存在的问题,用概率积分变换理论,给出了控制过程均值、过程方差的标准化控制图,适用于小批量生产环境下对过程均值、过程方差的有效控制。     

Statistical monitoring and control of a low defect process

Advanced technologies today are such that it is possible to keep the occurrence of defects in manufactured products at very low levels. The use of the conventional c-chart for statistical control of defects in such products would encounter serious practical difficulties because the low defect counts would render invalid the theoretical assumptions used in the construction of the chart. Based on reasoning with fundamental probability distributions, this paper offers a simple and reliable solution that is particularly suited to on-line inspection and testing operations such as those found in an automated manufacturing environment.     

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.     

航空测试实验室管理体系及过程控制方法探讨

通过对ISO/IEC17025:2005<检测和校准实验室能力的通用要求>及DILAC/AC01:2005<检测实验室和校准实验室能力认可准则>的深入研究,结合实际工作经验,提出了应用过程的分析方法,建立了航空测试实验室管理体系,既满足了标准的要求,又符合航空测试实验室的实际,实现了航空测试试验的过程控制,确保了测试结果的准确可靠.     

统计过程控制中基于数据深度的非参数变点控制图

为了解决多元非正态分布情况下的过程控制问题,提出基于数据深度的变点控制图,并对构建该控制图检验统计量的具体方法及控制流程进行了详细描述。为了检验该控制图的控制效果,采用服从二元伽马分布的样本数据对其进行了验证,并设置位置参数偏移范围为0.2至1.0,变点为14、24、34,几种情况分别检验其控制效果。数据仿真的结果表明:偏移越大,检测效果越好;偏移量小于0.7时,变点越大,检测效率越高;而当变点大于0.7时变点对检测效果的影响不明显。偏移量在0.1至0.4的范围内,变点越大,检测效果越好,但是这种边际效果在减小。     

计算机辅助统计过程控制的设计与实现

文章通过分析实现计算机辅助统计过程控制所需要解决的几个主要问题,提出了计算机辅助统计过程控制系统的整体功能设计方案,并对系统具体功能模块和系统关键技术实现方法进行了详细介绍,最后运用实例对系统的开发做了进一步的说明.     

Research into the use of statistical quality control in british manufacturing industry-part II

The work described in this paper is part of a large. Government-funded investigation into the usage of statistical methods of quality control (SQC) in British manufacturing industry. This paper reports the results of work in seventeen companies on the implementation of SQC. The results are summarized by the use of evaluation scales and attributes, and measures of success and barriers are identified. Some general conclusions are drawn which point to a methodology for the introduction of SQC.     

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。     

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.