Data-based latent variable methods for process analysis, monitoring and control

This paper gives an overview of methods for utilizing large process data matrices. These data matrices are almost always of less than full statistical rank, and therefore, latent variable methods are shown to be well suited to obtain useful subspace models from them for treating a variety of important industrial problems. An overview of the important concepts behind latent variable models is presented and the methods are illustrated with industrial examples in the following areas: (i) the analysis of historical databases and trouble-shooting process problems; (ii) process monitoring and FDI; (iii) extraction of information from novel multivariate sensors; (iv) process control in reduced dimensional subspaces. In each of these problems, latent variable models provide the framework on which solutions are based.