Statistical Monitoring and Fault Diagnosis of Batch Processes Using Two-Dimensional Dynamic Information

Two-dimensional (2D) dynamics widely exist in batch processes, which inspirit research efforts to develop corresponding monitoring schemes. Recently, two-dimensional dynamic principal component analysis (2D-DPCA) has been proposed to model and monitor such 2D dynamic batch processes, in which support region (ROS) determination is a key step. A proper ROS ensures modeling accuracy, monitoring efficiency, and reasonable fault diagnosis. The previous ROS determination method is practicable in many situations but still has certain limitations, as discussed in this paper. To overcome these shortcomings, a 2D-DPCA method with an improved ROS determination procedure is developed, by considering variable partial correlations and performing iterative stepwise regressions. Such a procedure expands ROS batch by batch and is a generalization of the autoregressive (AR) model order selection to the 2D batch process cases. Simulations show that the proposed method extracts 2D dynamics more accurately and improves the monitoring and diagnosis performance of the 2D-DPCA model