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An Indirect Kernel Optimization Approach to Fault Detection with KPCA

In: Mathematical Modeling and Computational Intelligence in Engineering Applications

Author

Listed:
  • 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)

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.

Suggested Citation

  • José M. Bernal de Lázaro & Orestes Llanes-Santiago & Alberto Prieto-Moreno & Diego Campos Knupp, 2016. "An Indirect Kernel Optimization Approach to Fault Detection with KPCA," Springer Books, in: Antônio José da Silva Neto & Orestes Llanes Santiago & Geraldo Nunes Silva (ed.), Mathematical Modeling and Computational Intelligence in Engineering Applications, chapter 0, pages 63-75, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-38869-4_5
    DOI: 10.1007/978-3-319-38869-4_5
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