Early fault detection via combining multilinear PCA with retrospective monitoring using weighted features

Current multivariate statistical process monitoring is mostly based on data-based models with the principal aim of detecting faults promptly. To increase fault detection performance, various methods, such as novel learners, sliding window-based methods, subspaces based on query point estimation residuals, and feature/component selection methods have been proposed. On the other hand, hierarchical and combined modeling have only been recently considered; furthermore, the online sampled observations, once assessed by the monitoring scheme, are not usually used again for fault detection. In the current study, we show how to obtain valuable information on faults via re-examining the recently sampled points in a conveniently built hierarchical monitoring scheme. The top level consists of a combination of a novel query point estimation method based on multilinear principal component analysis (PCA) and PCA model of the estimation residuals. Upon a warning signal from the upper level, the bottom level is implemented, that consists of retrospective PCA monitoring of the recently sampled observations, scaled with respect to estimation residuals. Implementation of the proposed scheme on a demonstrative process and Tennessee Eastman Plant data exhibits decrease both in fault detection delay and missed detection rate compared to both traditional and the recently proposed methods.