Nuclear power plant components condition monitoring by probabilistic support vector machine

Jie Liu (1, 2) , Redouane Seraoui (3) , Valeria Vitelli (1, 2) , Enrico Zio (1, 2)

1 LGI - Laboratoire Génie Industriel - EA 2606
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
3 EDF R&D - EDF R&D

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.

Domains: Engineering Sciences [physics]; Electric power; Statistics [stat]; Machine Learning [stat.ML]

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⟩