GOALI: Development of a Novel Subspace Approach to the Monitoring and Control of Chemical Processes

故障检测和诊断的一般理论框架将被开发。 主要内容包括故障识别，故障重建，并选择适当的模型尺寸。 一种新的子空间控制方法，可以提供纠正故障或干扰在真实的时间将开发。 故障检测将通过监测两个检测指标来完成。 平方预测误差指数检测残差子空间中的显著变化，并且Hotelling T2指数指示超出历史限制的操作。 提出了一种确定最佳故障构造主元个数的客观方法。 多元统计方法，包括主成分分析和偏最小二乘法，将用于分析数据，并将测量空间划分为正常和异常子空间。 由于高达40%的制造成本花费在维护和故障情况管理上，因此该项目将有可能应用于一般工业重要性的问题。 如果成功，该项目将实现以下四个目标：（1）通过故障重构和识别开发新的根本原因分析方法，（2）提供用于确定主分量的数量的客观的、良好建立的标准，（3）提供用于子空间控制和监测的集成方法，以及（4）建立用于故障检测、识别、重构的基础理论，以及主成分数目的确定。

A general theoretical framework for fault detection and diagnosis will be developed. The main elements include fault identification, fault reconstruction, and selection of the appropriate model dimensions. A novel subspace control approach that can provide corrections to faults or disturbances in real time will be developed. Fault detection will be accomplished by monitoring two detection indices. The squared prediction error index detects significant variability in the residual subspace, and the Hotelling T2 index indicates operation beyond the historical limits. An objective approach to determining the number of principal components for best fault construction is proposed. Multivariate statistical methods, including principal component analysis and partial least squares, will be used to analyze the data and partition the measurement space into a normal and an abnormal subspace. Because up to 40% of manufacturing costs are spent on maintenance and fault situation management, this project will have potential application to a problem of general industrial importance. If successful, the project will accomplish the following four goals: (1) develop a novel root-cause analysis approach by fault reconstruction and identification, (2) provide an objective, well-established criterion for determining the number of principal components, (3) provide an integrated approach for subspace control and monitoring, and (4) establish a fundamental theory for fault detection, identification, reconstruction, and determination of the number of principal components.