Authors: Chun-Ling Xie, Jen-Yuan Chang, Xiao-Cheng Shi, Jing-Min Dai
Addresses: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China. ' School of Engineering and Advanced Technology, Massey University, Albany, Auckland 0745, New Zealand. ' School of Automation, Harbin Engineering University, Harbin 150001, China. ' School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Abstract: This paper presents development of an automatic fault diagnosis system in the nuclear power plants to minimise the possible nuclear disasters caused by inaccurate diagnoses done by operators. Combined binary and decimal coding methods are employed in this work based on Radial Basis Function Neural Network (RBFNN) structure. This underlying RBFNN structure is further trained through genetic optimisation algorithm based on known frequent failure conditions from a nuclear power plant|s condensation and feed-water system. It is found that the proposed Genetic-RBFNN (GRBFNN) method not only makes the original neural network smaller in terms of computation and realisation but also improves the diagnosis speed and accuracy.
Keywords: fault diagnosis; nuclear power plants; NPP; RBF; neural networks; nuclear energy; radial basis function; genetic algorithms; failure conditions; condensation systems; feed-water systems; nuclear safety.
International Journal of Computer Applications in Technology, 2010 Vol.39 No.1/2/3, pp.159 - 165
Published online: 18 Aug 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article