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YUAN Can, CAI Qi, LIU Gang, YAN Xiang-wei, CHEN Yu-qing. Expert System Fault Diagnosis for Primary Circuit of Nuclear Power Plant Based on Neural Network[J]. Atomic Energy Science and Technology, 2014, 48(增刊1): 485-490. DOI: 10.7538/yzk.2014.48.S0.0485
Citation: YUAN Can, CAI Qi, LIU Gang, YAN Xiang-wei, CHEN Yu-qing. Expert System Fault Diagnosis for Primary Circuit of Nuclear Power Plant Based on Neural Network[J]. Atomic Energy Science and Technology, 2014, 48(增刊1): 485-490. DOI: 10.7538/yzk.2014.48.S0.0485
shu

Expert System Fault Diagnosis for Primary Circuit of Nuclear Power Plant Based on Neural Network

  • 1.

    Department of Nuclear Science and Engineering, Naval University of Engineering, Wuhan 430033, China

  • 2.

    Unit 95039, Shantou 515049, China

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    • Abstract
  • A fault diagnosis model which integrates the expert system into neural network (NN) was established. Through the study of the characteristic about production rule, the binary rule with ‘And’ was discussed for binary coding used in NN with high effectiveness. And the forward NN algorithm was used for reasoning, which avoided the conflict of knowledge. At the same time, the temporal NN results were saved for tracking after reasoning. The results indicate that the model with low misdiagnosis rate can meet the actual needs of fault diagnosis.
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    • References (5)
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    PATTON R J, CHEN J. Review of parity space approaches to fault diagnosis for aerospace system[J]. Journal of Guidance, Control and Dynamics, 1994, 17(2): 278-285.
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    姚华，单贵平，孙健国. 基于卡尔曼滤波器及神经网络的发动机故障诊断[J]. 航空动力学报，2008，23(6)：1111-1117.YAO Hua, SHAN Guiping, SUN Jianguo. Fault diagnosis for gas turbine engines based on Kalman filter and neural networks[J]. Journal of Aerospace Power, 2008, 23(6): 1111-1117（in Chinese）.
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