Abstract
Multivariate statistical representations have been widely used in the process manufacturing industries for process performance monitoring, in particular for the detection of changes in current operation and the onset of process disturbances or faults. Applications of the technology have focused to a lesser extent on manufacturing processes where drift occurs over time as part of normal process operation, e.g., due to reactor fouling, machine wear, ramping of temperatures during process operation, and changes due to set-point adjustments. In this paper, an extension to the methodology based on the statistical projection technique of principal component analysis (PCA) is proposed for the monitoring of processes where drift and set-points changes are common place, i.e., exponentially weighted PCA. The technique is illustrated through its application to a polymer film manufacturing process where the representation is required to adapt quickly to changes in the process that are part of normal operating procedures, but remain sensitive to the detection of deviations from normal operation.
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