Faculty Early Career Development: Multivariate Process Monitoring with Correlated Data

教师早期职业发展：使用相关数据进行多变量过程监控

• 批准号: 9501903
• 负责人: Christina Mastrangelo
• 金额: $ 29万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-06-01 至 2002-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9501903&HistoricalAwards=false
• 关键词: Faculty; Early; Career; Development; Multivariate

9501903 This research is focused on the quality control problem of understanding processes whose behaviors are affected by multiple, autocorrelated variables. Traditional statistical process control (SPC) is based on a single variable and the assumption of normality, independence, and homogeneous variance. This research capitalizes on the inappropriateness of these assumptions and the use of a single variable as a basis to understand and control a process when indeed in today's industrial environment there could be multiple, autocorrelated variables. The objective of the research is to systematically explore the behavior, sensitivity, and consistency of several multivariate quality control methods when autocorrelated multivariate data is present. These methods include the multivariate EWMA, principal component analysis, and multiple time series. The research was motivated by the lack of sufficient analytical tools to handle statistical process control situations in which multiple correlated factors influence the behavior of a process. The research plans to pursue the use of different forms of models to address the problem. The educational component of the research presents the investigator's teaching philosophy, methods of tracking teaching performance, and students understanding and involvement in the materials being taught. The investigator will deploy and integrate the research outcome into classroom by developing a new combined course in statistics and quality control with emphasis on the use of these techniques to solving industry-relevant problems in statistical process control. In addition, the techniques investigated in the research for the purpose of improving processes will be used to improve teaching effectiveness. The potential for the research is good. The outcome of the research can advance the body of knowledge in the broad area of multivariate process control. This will lead to a better understanding of process behavior and method of control. Improved control has a positive relationship to productivity and consequently, on competitiveness. By disseminating the research outcome through classroom instructions, students will gain an understanding of how process behavior is affected by process control parameters. They will also gain valuable experience on how to monitor and control processes in actual industrial settings.

9501903 本研究主要探讨了解过程的品质控制问题，此过程的行为受多重自相关变数影响。 传统的统计过程控制（SPC）是基于单变量和正态性、独立性和方差齐性的假设。 本研究利用这些假设的不适当性，并使用一个单一的变量作为基础来理解和控制的过程中，确实在今天的工业环境中可能有多个，自相关的变量。 本研究的目的是系统地探讨自相关多元数据时，几种多元质量控制方法的行为，灵敏度和一致性。 这些方法包括多变量EWMA，主成分分析和多个时间序列。 这项研究的动机是缺乏足够的分析工具来处理统计过程控制的情况下，多个相关因素影响的过程的行为。 该研究计划继续使用不同形式的模型来解决这个问题。 研究的教育部分介绍了研究者的教学理念，跟踪教学表现的方法，以及学生对所教材料的理解和参与。 调查员将部署和整合的研究成果到课堂上，通过开发一个新的统计和质量控制的组合课程，重点是使用这些技术来解决统计过程控制中的行业相关问题。 此外，在研究中调查的技术，以改善过程的目的将被用来提高教学效果。 研究的潜力是好的。 研究的结果可以在多变量过程控制的广阔领域中推进知识体系。 这将导致更好地理解过程行为和控制方法。 改进控制与生产力有积极的关系，因此，对竞争力。 通过课堂教学传播的研究成果，学生将获得过程行为是如何影响过程控制参数的理解。 他们还将获得如何在实际工业环境中监测和控制过程的宝贵经验。