MONITORING OF MULTIVARIABLE DYNAMIC PROCESSES AND SENSOR AUDITING

Industrial continuous processes are usually operated under closed-loop control, yielding process measurements that are autocorrelated, cross correlated, and collinear. A statistical process monitoring (SPM) met hod based on state variables is introduced to monitor such processes. The statistical model that describes the in-control variability is bas ed on a canonical variate (CV) state space model. The CV state variabl es are linear combinations of the past process measurements which expl ain the variability of the future measurements the most, and they are regarded as the principal dynamic dimensions. A T-2 statistic based on the CV state variables is utilized for developing the SPM procedure. The CV state variables are also used for monitoring sensor reliability . An experimental application to a high temperature short time (HTST) pasteurization process illustrates the proposed methodology. (C) 1998 Elsevier Science Ltd. All rights reserved.