A Time Window based Two-Dimensional PCA for Process Monitoring and Its Application to Tennessee Eastman Process

Multivariate statistical analysis methods like PCA have been widely utilized for fault diagnosis and quality control. Nevertheless, the traditional PCA based methods have some limitations in dealing with the dynamic data information which extensively exists in modern process industry. This paper developed a time window based 2D-PCA model to strengthen the capability of extracting dynamic features for process monitoring. First, this model uses the time window to construct 2-dimensional data matrices piece by piece to keep as much dynamic information as possible between samples. Second, 2D-PCA is applied to these two-dimensional data samples for dynamic feature learning. Finally, general statistics are calculated for online monitoring to detect abnormal states. Finally, Tennessee Eastman (TE) process is used to verify the effectiveness of the developed 2D-PCA monitoring strategy.