Abstract
Subspace method identification (SMI) and model reduction for Multivariate Statistical Process Control has been proposed as an improvement to dynamic principal component analysis (DPCA). The linear parametric model structure captures both static and dynamic information from the system. In this paper, an analysis of the dimension reduction capabilities of the subspace approach is provided. It is proven that the SMI method yields a parsimonious model structure that requires fewer latent variables and uses fewer process measurements than DPCA. These findings are illustrated by an industrial application study.
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