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Journal Articles Annals of Nuclear Energy Year : 2013

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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Dates and versions

hal-00790421 , version 1 (12-06-2013)

Identifiers

  • HAL Id : hal-00790421 , version 1

Cite

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