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 T-2 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.