Uncertain dynamic process monitoring using moving window PCA for interval-valued data

In this paper, we present a new process monitoring approach for uncertain, or highly noisy systems, which is based on the well known Moving Window Principal Component Analysis (MWPCA) extended to the interval case. We propose to use The Midpoints-Radii PCA (MRPCA) for modelling, which independently exploits two PCAs on the center and radius matrices of the system’s sensor interval-valued data. Furthermore, by changing the size and the shift of the window, Both center and radius model parameters are updated on-line; thus deriving a new Moving Window Midpoints-Radii PCA (MWMRPCA) approach. Based on the updated MWMRPCA, an interval SPE statistic and its control limit are calculated and updated through time, and are used for monitoring the state of the process. The performances of the proposed approach is illustrated by an application to the detection of faults on the Tennesse Eastman Process (TEP).