Nuclear power plant components condition monitoring by probabilistic support vector machine

被引：67

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

页码：23 / 33

页数：11

共 55 条

[1] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION
AKAIKE, H
[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) : 716 - 723
[2] [Anonymous], 2004, Introduction to Machine Learning
[3] [Anonymous], 1977, Solution of illposed problems
[4] [Anonymous], 1995, MACHINE LEARNING
[5] Calculation of the power peaking factor in a nuclear reactor using support vector regression models
Bae, In Ho
Na, Man Gyun
Lee, Yoon Joon
Park, Goon Cherl
[J]. ANNALS OF NUCLEAR ENERGY, 2008, 35 (12) : 2200 - 2205
[6] Baraldi P., 2010, NPIC HMIT 2010 LAS V
[7] Genetic algorithm-based wrapper approach for grouping condition monitoring signals of nuclear power plant components
Baraldi, Piero
Canesi, Roberto
Zio, Enrico
Seraoui, Redouane
Chevalier, Roger
[J]. INTEGRATED COMPUTER-AIDED ENGINEERING, 2011, 18 (03) : 221 - 234
[8] Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401
[9] A tutorial on Support Vector Machines for pattern recognition
Burges, CJC
[J]. DATA MINING AND KNOWLEDGE DISCOVERY, 1998, 2 (02) : 121 - 167
[10] Applying support vector machine to predict the critical heat flux in concentric-tube open thermosiphon
Cai, Jiejin
[J]. ANNALS OF NUCLEAR ENERGY, 2012, 43 : 114 - 122

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