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Some issues in statistical process control: Change-point techniques.

机译：统计过程控制中的一些问题：变更点技术。

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摘要

Quality improvement and Statistical Quality Control face many challenges. A major issue in statistical process control is the detection of changes in the location and in scale parameters of a process with unknown mean (μ o) and unknown standard deviation (σ0) that undergoes a small shift of unknown size (δ). Small shifts in quality deserve more than a usual attention and failure to diagnose them if they persist for a long time can be more damaging than large shifts. These challenges however become crucial when faced with multivariate processes (more than 2 quality characteristics), and when the multivariate process performance is based on the behavior of a set of interrelated variables. These problems are further compounded by a lack of sophisticated tools especially when it comes to processes with modest data to start with, thereby misleading engineers in the calibration step of processes. Along with these, there is also a need of avoiding the harmful effect of imprecision in the parameter estimation and solving the dichotomy between the phase I (calibrating) and phase II (charting) methods in quality.; Even though there exist several optimal techniques to capture small shifts in quality, they are restrictive in that they require an advance knowledge of the true parameters of the process (information frequently unavailable). An attractive technique that will not require knowledge about the parameters is the change-point formulation. Our approach to the change-point technique advances the classical fix-sample or static formulation. We develop an approach based on sequential estimates using a dynamic procedure.; Both univariate and multivariate aspects of this new approach have been covered. We also suggest a moving window with update approach in large sample situation.; The benefits we have found from this work are: Solving the problem of small shifts in Industrial Processes; removing the (sometimes artificial) dichotomy between phase I and phase II methods in quality; and finding a multivariate tool for both processes with small and large historical data set.
机译：质量改进和统计质量控制面临许多挑战。统计过程控制中的一个主要问题是检测平均值（μ o ）未知且标准偏差未知（σ< sub> 0 ）经历了一个未知大小（δ）的小位移。小幅度的变化比通常的关注更值得关注，并且如果它们持续很长的时间而无法对其进行诊断，则比大的变化更具破坏性。但是，当面对多变量过程（两个以上的质量特征），并且多变量过程的性能基于一组相互关联的变量的行为时，这些挑战就变得至关重要。这些问题由于缺乏复杂的工具而变得更加复杂，尤其是在开始使用少量数据的过程中，从而在过程的校准步骤中误导了工程师。除此之外，还需要避免参数估计中不精确的有害影响，并解决质量第一阶段（校准）和第二阶段（图表）方法之间的二分法。即使存在几种捕获质量细微变化的最佳技术，但它们还是有局限性的，因为它们需要事先了解过程的真实参数（信息经常不可用）。不需要参数知识的一种有吸引力的技术是 change-point 公式。我们对变化点技术的方法改进了经典的固定样本或静态公式。我们使用动态过程基于顺序估计来开发一种方法。这种新方法的单变量和多变量方面都已涵盖。我们还建议在大样本情况下使用移动窗口进行更新方法。我们从这项工作中发现的好处是：解决了工业过程中的微小变动问题；在质量上消除I期和II期方法之间的（有时是人工的）二分法；并为具有较小和较大历史数据集的过程找到一个多元工具。

著录项

  • 作者

    Zamba, Kokou Dovi.;

  • 作者单位

    University of Minnesota.;

  • 授予单位 University of Minnesota.;
  • 学科 Statistics.; Engineering System Science.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 155 p.
  • 总页数 155
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 统计学;系统科学;
  • 关键词

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