Application of RBF Artificial Neural Network to Fault Diagnose in Nuclear Power Plant

XIONG Jin-kui; XIE Chun-ling; SHI Xiao-cheng; ZHANG Hong-guo; SUN Tie-li

Jun. 2006

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Article Navigation > Nuclear Power Engineering > 2006 > 27(3): 57-60,96

XIONG Jin-kui, XIE Chun-ling, SHI Xiao-cheng, ZHANG Hong-guo, SUN Tie-li. Application of RBF Artificial Neural Network to Fault Diagnose in Nuclear Power Plant[J]. Nuclear Power Engineering, 2006, 27(3): 57-60,96.

Citation:

XIONG Jin-kui, XIE Chun-ling, SHI Xiao-cheng, ZHANG Hong-guo, SUN Tie-li. Application of RBF Artificial Neural Network to Fault Diagnose in Nuclear Power Plant[J]. Nuclear Power Engineering, 2006, 27(3): 57-60,96.

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Application of RBF Artificial Neural Network to Fault Diagnose in Nuclear Power Plant

1. College of Power and Nuclear Energy, Harbin Engineering University, 150001, China;
2. Jiujiang Subsection of NO. 707 Research Institute, China Ship Building Industry Corporation, Jiujiang, Jiangxi, 332007, China

Received Date: 2004-12-20
Rev Recd Date: 2005-03-11

Available Online: 2025-07-29

Abstract

Abstract

Some faults of condensation and feed water system in nuclear power plants are analyzed and a fault knowledge base is established based on the experts’ knowledge at the same time. RBF artificial neural network is introduced to the fault diagnose in nuclear power plants. Because of the use of dynamic designing method of RBF network, not only the scale of RBF artificial neural network is smaller, but also its generalization ability is higher, which improve the speed and accuracy of diagnose system. Finally a fault diagnose system is founded by VC++.
- RBF artificial neural network,
- Fault diagnose,
- Nuclear power plant