New Fusion Architectures for Performance Enhancement of a PCA-Based Fault Diagnosis and Isolation System

Abstract

This paper proposes some new fusion architectures to enhance the diagnostic operation of a principal component analysis (PCA)-based fault diagnosis and isolation (FDI) system. The first approach presents a serial classifier fusion methodology by incorporating a support vector machine (SVM) classifier to diagnose PCA-based fault features. Then, parallel fusion architecture is proposed to fuse the fault diagnostics due to the individual SVM, Bayes and K-nearest neighbor (K-NN) classifiers. The two series and parallel fusion architectures are finally incorporated into a new combined framework to yield a more efficient and powerful diagnostic capabilities. Extensive simulation test experiments are conducted to demonstrate the comparative performances of the new proposed fusion-based FDI systems on the Tennessee Eastman (TE) process plant as a large-scale benchmark problem.