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
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