Batch Process Monitoring in Score Space of Two-Dimensional Dynamic Principal Component Analysis (PCA)

Two-dimensional dynamic principal component analysis (2-D-DPCA) is a recent developed method for two-dimensional (2-D) dynamic batch process monitoring. However, it only utilizes residual information in fault detection and information in score space is wasted, which may compromise the monitoring efficiency. In this paper, 2-D multivariate score autoregressive (AR) filters are designed to remove the 2-D dynamics retained in score space and make the filtered scores obey certain statistical assumptions, so that the T2 statistic can be calculated reasonably for process monitoring. Simulation shows that using the filters enhances the monitoring efficiency while reducing the chances of false alarms and missed alarms.