Reference set selection with generalized orthogonal Procrustes analysis for multivariate statistical process monitoring of multiple production processes

Abstract

Multivariate process monitoring is important in industry to ensure that production processes perform as close as possible to optimal operation. However, the selection of a reference set of optimal or expected performance is required for efficient process monitoring in real time. In this paper we present the method of generalized orthogonal Procrustes analysis to select a reference set for the multivariate monitoring of multiple production processes simultaneously. We combine generalized orthogonal Procrustes analysis with principal component analysis (PCA) and biplots to illustrate the implementation of the method and the interpretation of the results which provide important information on the relationships between many process variables and differences between the production processes. The work is motivated by an industrial problem involving the multivariate monitoring of a coal gasification production facility considering many process variables monitored across multiple reactors.