Improvement of fault diagnosis efficiency in nuclear power plants using hybrid intelligence approach

被引：20

作者：

Liu, Yong-kuo ^{[1
]}

Xie, Chun-li ^{[2
]}

Peng, Min-jun ^{[1
]}

Ling, Shuang-han ^{[3
]}

机构：

[1] Harbin Engn Univ, Fundamental Sci Nucl Safety & Simulat Technol Lab, Harbin 150001, Peoples R China

[2] Northeast Forestry Univ, Traff Coll, Harbin 150040, Peoples R China

[3] Suzhou Nucl Power Res Inst, Suzhou 215004, Peoples R China

来源：

PROGRESS IN NUCLEAR ENERGY | 2014年 / 76卷

基金：

中国国家自然科学基金;

关键词：

Fault diagnosis; Nuclear power plant; Artificial neural network; Data fusion; Signed directed graph (SDG); SYSTEM; IDENTIFICATION; MODEL;

D O I：

10.1016/j.pnucene.2014.05.001

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：

Different types of faults could occur in a nuclear power plant, and there was no direct correspondence between a specific fault and its symptoms. So, a hybrid intelligence approach is proposed for the fault diagnosis at a nuclear power plant. Depending up the symptoms observed and the progress of fault diagnosis process, different fault diagnosis technologies, such as artificial neural network, data fusion and signed directed graph, could be combined as appropriate to detect and identify different faults at local or global level in nuclear power plants. The effectiveness of hybrid intelligence approach in improving the fault diagnosis efficiency in nuclear power plants was verified through simulation experiments. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：122 / 136

页数：15

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