Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2017, Vol. 28 ›› Issue (3): 34 doi: 10.1007/s41365-017-0184-0

• NUCLEAR ENERGY SCIENCE AND ENGINEERING • Previous Articles     Next Articles

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Yue Zhao 1  Francesco Di Maio 2  Enrico Zio 2,3  Qin Zhang 1,4  Chun-Ling Dong 4  Jin-Ying Zhang 5   

  1. 1 Institute of Nuclear and New Energy Technology of Tsinghua University, Beijing 100084, China
    2 Energy Department, Politecnico di Milano, 20156 Milan, Italy
    3 Chair System Science and the Energy Challenge, Fondation Electricite′ de France (EDF), CentraleSupe′lec, Universite′ Paris Saclay, 92290 Chatenay-Malabry, France
    4 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
    5 Department of Economic and Management, North China Electric Power University, Beijing 102206, China
  • Contact: Yue Zhao E-mail:zhaoyue0803@126.com
Yue Zhao, F.Di Maio, Enrico Zio, Qin Zhang, Chun-Ling Dong, Jin-Ying Zhang. Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant.Nuclear Science and Techniques, 2017, 28(3): 34     doi: 10.1007/s41365-017-0184-0

Abstract:

Fault diagnostics is important for safe operation of nuclear power plants (NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph (DUCG) has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is
complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph
construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree (FDT) by extracting from the DUCG a
fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm (GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

Key words: Dynamic uncertain causality graph, Fault diagnosis, Classification, Fuzzy decision tree, Genetic algorithm, Nuclear power plant