TY - JOUR
T1 - Nuclear power plant components condition monitoring by probabilistic support vector machine
AU - Liu, Jie
AU - Seraoui, Redouane
AU - Vitelli, Valeria
AU - Zio, Enrico
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Condition monitoring
KW - Nuclear power plant
KW - Point prediction
KW - Probabilistic support vector machine
UR - https://www.scopus.com/pages/publications/84873648199
U2 - 10.1016/j.anucene.2013.01.005
DO - 10.1016/j.anucene.2013.01.005
M3 - 文章
AN - SCOPUS:84873648199
SN - 0306-4549
VL - 56
SP - 23
EP - 33
JO - Annals of Nuclear Energy
JF - Annals of Nuclear Energy
ER -