An Indirect Kernel Optimization Approach to Fault Detection with KPCA

José M. Bernal de Lázaro (), Orestes Llanes-Santiago (), Alberto Prieto-Moreno () and Diego Campos Knupp ()
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José M. Bernal de Lázaro: Instituto Superior Politécnico José Antonio Echeverri̧a (CUJAE), Reference Center for Advanced Education
Orestes Llanes-Santiago: Instituto Superior Politécnico José Antonio Echeverri̧a (CUJAE), Automatic and Computing Department
Alberto Prieto-Moreno: Instituto Superior Politécnico José Antonio Echeverri̧a (CUJAE), Automatic and Computing Department
Diego Campos Knupp: Polytechnic Institute, IPRJ-UERJ, Mechanical Engineering and Energy Department

Chapter Chapter 5 in Mathematical Modeling and Computational Intelligence in Engineering Applications, 2016, pp 63-75 from Springer

Abstract: Abstract This chapter discusses a new indirect kernel optimization criterion for the adjustment of a fault detection process that is based on the dimension–reduction technique known as kernel principal component analysis. The kernel parameter optimization proposed here involves the computation of the false alarm rate and false detection rate indicators that are combined in a single indicator: the area under the ROC curve. This approach was tested on the Tennessee Eastman (TE) process, where a significant decrease in false and missing alarms was observed.

Keywords: Fault detection; Kernel PCA; Indirect optimization; AUC; Tennessee Eastman; Curve ROC historical data; False detection rate; False alarm rate (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-38869-4_5

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DOI: 10.1007/978-3-319-38869-4_5

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