Dynamic principal component analysis using subspace model identification

Abstract

This work analyses a recently proposed statistically based technique for monitoring complex dynamic process systems [17]. The technique utilises a state space model that is cast into the multivariate statistical process control framework (i) to define a set of state variables that can describe dynamic process behaviour, (ii) to generate univariate statistics that can monitor dynamic process behaviour and (iii) to construct contribution plots from these statistics that can diagnose anomalous process behaviour. The presented analysis reveals that the size of the state space monitoring model can be reduced. The utility of the improved dynamic monitoring technique is demonstrated using an industrial application study to a glass-melter process. © Springer-Verlag Berlin Heidelberg 2005.