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

页码：23 / 33

页数：11

共 55 条

[41] A tutorial on support vector regression [J].

Smola, AJ ;

Schölkopf, B .

STATISTICS AND COMPUTING, 2004, 14 (03) :199-222

[42]

Sollich P., 1999, TECHNICAL REPORT

[43] Support vector regression model for the estimation of γ-ray buildup factors for multi-layer shields [J].

Trontl, Kresimir ;

Smuc, Tomislav ;

Pevec, Dubravko .

ANNALS OF NUCLEAR ENERGY, 2007, 34 (12) :939-952

[44]

Vapnik V., 1995, The nature of statistical learning theory

[45]

Vapnik V.N., 1996, P 10 NEUR INF PROC S

[46] Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities [J].

Venkatasubramanian, V .

COMPUTERS & CHEMICAL ENGINEERING, 2005, 29 (06) :1253-1263

[47]

Vladimir C., 1998, LEARNING DATA CONCEP

[48] Prognosis of machine health condition using neuro-fuzzy systems [J].

Wang, WQ ;

Golnaraghi, MF ;

Ismail, F .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2004, 18 (04) :813-831

[49]

Williams CKI, 1997, ADV NEUR IN, V9, P295

[50] On-Line Fault Recognition System for the Analogic Channels of VVER 1000/400 Nuclear Reactors [J].

Yazikov, Mikhail ;

Gola, Giulio ;

Berg, Oivind ;

Porsmyr, Jan ;

Valseth, Helge ;

Roverso, Davide ;

Hoffmann, Mario .

IEEE TRANSACTIONS ON NUCLEAR SCIENCE, 2012, 59 (02) :411-418

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