Quality-related fault detection using linear and nonlinear principal component regression

The issue of quality-related fault detection is a hot research topic in the process monitoring community in the recent five years. Several modifications based on partial least squares (PLS) have been proposed to solve the relevant problems for linear systems. For the systems with nonlinear characteristics, some modified algorithms based on kernel partial least squares (KPLS) have also been designed very recently. However, most of the existing methods suffer from the defect that their performances are not stable when the fault intensity increases. More importantly, there is no way yet to solve the linear and nonlinear problems in a uniform algorithm structure, which is very important for simplifying the design steps of fault detection systems. This paper aims to propose such approaches based on principal component regression (PCR) and kernel principal component regression (KPCR). Such that, relevant problems in linear and nonlinear systems can be solved in the same way. Two literature examples are used to test the performance of the proposed approaches. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.