Recent results on key performance indicator oriented fault detection using the DB-KIT toolbox

Abstract

A new MATLAB toolbox DB-KIT was recently developed for the design and implementation of fault diagnosis systems. For the purpose of key performance indicator (KPI) oriented fault detection, over the past few years, a series of test statistics and the corresponding thresholds were derived based on the modified data structures originating from the existing multivariate statistical analysis tools. These data-driven approaches are numerically reliable, efficient and of high fault detection performance. Especially, the false alarm rates (FARs) under KPI-unrelated fault scenarios are suppressed with great efforts, which is the central task of the KPI-oriented fault detection problem. DB-KIT was firstly introduced at the 2016 IEEE Industrial Electronics Conference, and the initial results on algorithm efficiency and fault detection performance were reported in Comparison of KPI related fault detection algorithms using a newly developed MATLAB toolbox: DB-KIT with simulation tests on the Tennessee Eastman Process benchmark. This paper reports more recent results on a widely used numerical test and on a close-loop configured three-stage hot rolling mill process to reveal the performance of the algorithms at the extreme faulty conditions, and demonstrates the performance under the plant-wide performance supervised framework.