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Nuclear power plant components condition monitoring by probabilistic support vector machine

Abstract : In this paper, an approach for the prediction of the condition of Nuclear Power Plant (NPP) components is proposed, for the purposes of condition monitoring. It builds on a modified version of the Probabilistic Support Vector Regression (PSVR) method, which is based on the Bayesian probabilistic paradigm with a Gaussian prior. Specific techniques are introduced for the tuning of the PSVR hyerparameters, the model identification and the uncertainty analysis. A real case study is considered, regarding the prediction of a drifting process parameter of a NPP component.
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https://hal-supelec.archives-ouvertes.fr/hal-00790421
Contributor : Yanfu Li <>
Submitted on : Wednesday, June 12, 2013 - 12:54:25 PM
Last modification on : Wednesday, April 8, 2020 - 4:09:11 PM
Document(s) archivé(s) le : Friday, September 13, 2013 - 2:35:12 AM

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  • HAL Id : hal-00790421, version 1

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Jie Liu, Redouane Seraoui, Valeria Vitelli, Enrico Zio. Nuclear power plant components condition monitoring by probabilistic support vector machine. Annals of Nuclear Energy, Elsevier Masson, 2013, 56, pp.23-33. ⟨hal-00790421⟩

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