Statistical Process Control of Processes Generating Time Dependent Data

此拨款为过程工业（即化学过程）中常见的产生随时间变化的数据的过程的质量监测和控制的有效和高效框架的开发提供资金。首先，将分析开发经验共变图，并评估其绩效潜力。其次，统计过程控制（SPC）和自动过程控制之间的接口将进一步开发超出了什么是在文献中发现。第三，其他新的和现有的SPC方法将在时间相关数据特有的新过程干扰范式的背景下进行评估。第四，将对递归离散时间卡尔曼滤波器（RDTKF）进行修改并评估其在该场景中的应用。如果成功，这个项目将为过程工业的质量监视和控制提供一个有凝聚力的工具设计。由此产生的框架应适用于许多制造过程，并将根据分析和模拟结果进行良好的测试。具体方案的优势和劣势将在研究议程的任务中记录。研究的影响将通过在学术期刊上的传播和在各个行业的应用来实现。

9732831 English This grant provides funding for the development of an effective and efficient framework for the quality monitoring and control of processes generating time dependent data common to the process industries (i.e., chemical processes). First, the empirical covariation chart will be analytically developed, and its performance potential will be evaluated. Second, the interface between statistical process control (SPC) and automatic process control will be further exploited beyond what is found in the literature. Third, other new and existing SPC approaches will be evaluated in the context of a new process disturbance paradigm unique to time dependent data. Fourth, the recursive discrete time Kalman filter (RDTKF) will be modified and evaluated for application in this scenario. If successful, this project will provide a cohesive design of tools for quality monitoring and control for the process industries. The resulting framework should be suitable for application in many manufacturing processes and will be well tested based upon analytical and simulation results. Scenario specific strengths and weaknesses will be documented within the tasks of the research agenda. The impact of the research will be realized through the dissemination of findings in scholarly journals and applications within various industries.