| Abstract
| - 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 faultdetection and information in score space is wasted, which may compromise the monitoring efficiency. In thispaper, 2-D multivariate score autoregressive (AR) filters are designed to remove the 2-D dynamics retainedin score space and make the filtered scores obey certain statistical assumptions, so that the T2 statistic can becalculated reasonably for process monitoring. Simulation shows that using the filters enhances the monitoringefficiency while reducing the chances of false alarms and missed alarms.
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