Nuclear power plant components condition monitoring by probabilistic support vector machine

被引：70
作者：
Liu, Jie [1 ,2 ]
Seraoui, Redouane [3 ]
Vitelli, Valeria [1 ,2 ]
Zio, Enrico [1 ,2 ,4 ]
机构：
[1] Ecole Cent Paris, European Fdn New Energy Elect France, Chair Syst Sci & Energet Challenge, Chatenay Malabry, France
[2] Supelec Ecole Super Elect, Gif Sur Yvette, France
[3] EDF R&D, Simulat & Informat TEchnol Power Generat Syst STE, Chatou, France
[4] Politecn Milan, Dept Energy, I-20133 Milan, Italy
来源：
ANNALS OF NUCLEAR ENERGY | 2013年 / 56卷
关键词：
Probabilistic support vector machine; Condition monitoring; Nuclear power plant; Point prediction; THINNED PIPE BENDS; FAULT-DIAGNOSIS; STEAM-GENERATOR; HTGR COMPONENTS; REGRESSION; CLASSIFICATION; SYSTEM; MODEL;
D O I：
10.1016/j.anucene.2013.01.005
中图分类号：
TL [原子能技术]; O571 [原子核物理学];
学科分类号：
0827 ; 082701 ;
摘要：
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. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码：23 / 33
页数：11
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