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.
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Acknowledgements
The authors thank the financial support provided by the Brazilians Agencies FAPERJ, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico; CAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, and MES/CUBA, Ministerio de Educación Superior de Cuba.
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de Lázaro, J.M.B., Llanes-Santiago, O., Prieto-Moreno, A., Campos Knupp, D. (2016). An Indirect Kernel Optimization Approach to Fault Detection with KPCA. In: Silva Neto, A., Llanes Santiago, O., Silva, G. (eds) Mathematical Modeling and Computational Intelligence in Engineering Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-38869-4_5
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DOI: https://doi.org/10.1007/978-3-319-38869-4_5
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