Abnormal Situation Detection, Three Way Data and Projection Methods; Robust Modeling for Industrial Applications

Abstract

Three - way data collected from batch processes and from transitions of continuous processes are dynamic in nature; the process variables in such processes are both auto correlated and cross correlated. Empirical models developed for the statistical process control of these processes should be capable of capturing the auto and cross correlation of the process variables. Statistical process control checks deviations from a nominal behaviour. Therefore for the statistical process control of batch processes and transitions we should look at deviations of process variable trajectories from their nominal trajectories and from their nominal auto/cross correlations. This paper addresses issues related to modelling three way data collected from such processes using projection methods, such as principal component analysis (PCA) and partial least squares (PLS).