In this article, the monitoring of continuous processes using linear dynamic models is presented. It is outlined that dynamic extensions to conventional multivariate statistical process control (MSPC) models may lead to the inclusion of large numbers of variables in the condition monitor. To prevent this, a new dynamic monitoring scheme, based on subspace identification, is introduced, which can (i) determine a set of state variable for describing process dynamics and (ii) produce a reduced set of variables to monitor process performance. This is demonstrated by an application study to a realistic simulation of a chemical process.

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