Data Structures for Multivariate Statistical Process Control

9713518 Runger 该奖项提供资金来制定多元统计过程控制的数据结构指南。过程控制算法应尽快检测过程异常，而不会产生过多的误报。然而，在实践中，（1）人们通常可以从许多可用变量（甚至数百个）中进行选择，（2）原始数据的时间顺序不适合直接输入到算法中，需要变量同步，（3）变量具有不同的测量频率，（4）某些变量比其他变量对过程干扰更敏感，（5）某些变量可以作为其他变量的函数进行计算，以及（6）所选变量的划分对于降低复杂性、提高性能和简化控制算法的解释非常重要。过程数据和过程扰乱的数学描述将用于制定预处理数据的建议，以解决这些问题。过程数据将用于验证建议。 如果成功，该项目将影响为过程控制而收集的过程数据的类型和结构。这将包括记录的过程测量、测量的频率、测量的时间顺序以及如何将测量分配（或分区）到特定的控制算法。初步研究表明，数据结构是过程控制策略的重要元素，随着分析变量数量的增加，数据结构将成为关键元素。该项目的建议将提供数据结构和控制算法之间的协同作用，以提高控制方法的实用性、应用和性能。研究结果还将适用于神经网络、数据挖掘、信号处理以及过程控制等领域的数据结构。

9713518 Runger This award provides funding to develop guidelines for a data structure for multivariate statistical process control. A process control algorithm should detect a process anomaly as quickly as possible, without incurring excessive false alarms. However, in practice, (1) one can often select from among many variables (even hundreds) that are available, (2) the time order of the original data is not suitable for direct input into the algorithm, a synchronization of variables is needed, (3) variables have different measurement frequencies, (4) some variables are more responsive to process upsets than others, (5) some variables can be computed as functions of the others, and (6) a partition of selected variables is important to reduce complexity, improve performance, and simplify the interpretation of the control algorithm. A mathematical description of process data and process upsets will be used to develop recommendations for preprocessing the data to address these issues. Process data will be used to validate recommendations. If successful, this project will affect the type and structure of process data that is collected for process control. This will include the process measurements that are recorded, the frequency of measurements, the time ordering of measurements, as well as how the measurements will be allocated (or partitioned) to particular control algorithms. Preliminary research has shown that a data structure is an important element of a process control strategy that becomes a critical element as the number of variables under analysis increase. Recommendations from this project will provide a synergy between the data structures and the control algorithms to increase the usefulness, application, and performance of control methods. The results will also apply to data structures in areas such as neural networks, data mining, and signal processing, as well as process control.