Sensitivity Enhancing Transformations for Large-Scale Process Monitoring

A new pre-processing methodology is proposed for improving the detection capability to changes in process structure. It is named sensitivity enhancing transformation (SET), and uses information of the causal network topology underlying the measured process variables in order to construct a set of uncorrelated transformed variables around which the detection of changes in the variables correlation structure is maximized. A new group of monitoring statistics, based on partial correlations, is also presented that take full advantage of the SET features. The use of partial correlations as an association measure provides a finer map of the connectivity between process variables even without attributing any causal directionality. The availability of such a finer association map potentiates the development of more sensitive schemes for detecting structural changes, such as the ones proposed in this work. The results obtained in the comparison study involving other current methodologies for monitoring the correlation structure, show that the proposed methods are able to effectively detect changes in the systems structure and presented higher sensitivity when compared to the current monitoring statistics tested.