Enhanced graph-based fault diagnostic system for nuclear power plants

  • Yong-Kuo LiuEmail author
  • Xin Ai
  • Abiodun Ayodeji
  • Mao-Pu Wu
  • Min-Jun Peng
  • Hong Xia
  • Wei-Feng Yu


Scheduled maintenance and condition-based online monitoring are among the focal points of recent research to enhance nuclear plant safety. One of the most effective ways to monitor plant conditions is by implementing a full-scope, plant-wide fault diagnostic system. However, most of the proposed diagnostic techniques are perceived as unreliable by operators because they lack an explanation module, their implementation is complex, and their decision/inference path is unclear. Graphical formalism has been considered for fault diagnosis because of its clear decision and inference modules, and its ability to display the complex causal relationships between plant variables and reveal the propagation path used for fault localization in complex systems. However, in a graph-based approach, decision-making is slow because of rule explosion. In this paper, we present an enhanced signed directed graph that utilizes qualitative trend evaluation and a granular computing algorithm to improve the decision speed and increase the resolution of the graphical method. We integrate the attribute reduction capability of granular computing with the causal/fault propagation reasoning capability of the signed directed graph and comprehensive rules in a decision table to diagnose faults in a nuclear power plant. Qualitative trend analysis is used to solve the problems of fault diagnostic threshold selection and signed directed graph node state determination. The similarity reasoning and detection ability of the granular computing algorithm ensure a compact decision table and improve the decision result. The performance of the proposed enhanced system was evaluated on selected faults of the Chinese Fuqing 2 nuclear reactor. The proposed method offers improved diagnostic speed and efficient data processing. In addition, the result shows a considerable reduction in false positives, indicating that the method provides a reliable diagnostic system to support further intervention by operators.


Nuclear power plants Fault diagnosis Signed directed graph Decision table Granular computing 


  1. 1.
    Y. Fujita, Learning from the Fukushima nuclear power plant accident—a resilience point of view, in 2012 Southeast Asian Network of Ergonomics Societies Conference (SEANES) (2012), pp. 1–5.
  2. 2.
    V. Venkat, R. Raghunathan, Y. Kewen et al., A review of process fault detection and diagnosis part I: quantitative model-based methods. Comput. Chem. Eng. 27(3), 313–326 (2003). CrossRefGoogle Scholar
  3. 3.
    G. Xie, X. Wang, K. Xie, SDG-based fault diagnosis and application based on reasoning method of granular computing, in IEEE Conf. Cont. Dec. (2010), pp. 1718–1722.
  4. 4.
    A. Ayodeji, Y.K. Liu, H. Xia, Knowledge-base operator support system for nuclear power plant fault diagnosis. Prog. Nucl. Energy 105, 42–50 (2018). CrossRefGoogle Scholar
  5. 5.
    Y.K. Liu, A. Ayodeji, Z.-B. Wen et al., A cascade intelligent fault diagnostic technique for nuclear power plants. J. Nuclear Sci. Technol. 55(3), 1–13 (2018). CrossRefGoogle Scholar
  6. 6.
    A. Ayodeji, Y.K. Liu, SVR optimization with soft computing algorithms for incipient SGTR diagnosis. Ann. Nuclear Energy 121, 89–100 (2018). CrossRefGoogle Scholar
  7. 7.
    M.A. Kramer, B.L. Palowitch Jr., A rule-based approach to fault diagnosis using the signed directed graph. Am. Inst. Chem. Eng. J. 33(7), 1067–1078 (1987). MathSciNetCrossRefGoogle Scholar
  8. 8.
    Z. Zhang, C. Wu, B. Zhang et al., SDG multiple fault diagnosis by real-time inverse inference. Rel. Eng. Syst. Saf. 87(2), 173–189 (2005). MathSciNetCrossRefGoogle Scholar
  9. 9.
    G.H. Wu, Y.K. Liu, C.L. Xie, Research on fault diagnosis based on SDG-QTA in nuclear power plants. Atom. Energy Sci. Technol. 50(8), 1467–1473 (2016). (in Chinese) CrossRefGoogle Scholar
  10. 10.
    R. Smaili, R.E. Harabi, M.N. Abdelkrim, Design of fault monitoring framework for multi-energy systems using signed directed graph. IFACPapersOnline 50(1), 15734–15739 (2017). CrossRefGoogle Scholar
  11. 11.
    R.C. Brewster, F.F. Foucaud et al., The complexity of signed graph and edge-coloured graph homomorphisms. Discrete Math. 340(2), 223–235 (2017). MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    B. He, T. Chen, X. Yang, Root cause analysis in multivariate statistical process monitoring: Integrating reconstruction-based multivariate contribution analysis with fuzzy-signed directed graphs. Comput. Chem. Eng. 64, 167–177 (2014). CrossRefGoogle Scholar
  13. 13.
    G. Chiaselotti, T. Gentile, F. Infusino, Granular computing on information tables: families of subsets and operators. Inf. Sci. 442, 72–102 (2018). MathSciNetCrossRefGoogle Scholar
  14. 14.
    S. Butenkova, A. Zhukova, A. Nagoro et al., Granular computing models and methods based on the spatial granulation. Proc. Comput. Sci. 103, 295–302 (2017). CrossRefGoogle Scholar
  15. 15.
    M. Wang, M. Wei, Y. Feng, An iterative algorithm for least squares problem in quaternionic quantum theory. Comput. Phys. Commun. 179(4), 203–207 (2008). MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Y.Y. Yao, Information granulation and rough set approximation. Int. J. Intell. Syst. 16(1), 87–104 (2001).;2-S CrossRefzbMATHGoogle Scholar
  17. 17.
    Y. Yao, Y. Zhao, Attribute reduction in decision-theoretic rough set models. Inf. Sci. 178(17), 3356–3373 (2008). MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Y.K. Liu, G.H. Wu, C.L. Xie, A fault diagnosis method based on signed directed graph and matrix for nuclear power plants. Nucl. Eng. Des. 297, 166–174 (2016). CrossRefGoogle Scholar
  19. 19.
    T.Y. Lin, Granular computing on binary relations II: rough set representations and belief functions. Rough Sets Knowl. Discov. 1, 121–140 (1998)Google Scholar
  20. 20.
    X. Hu, N. Cercone, Learning in relational databases: a rough set approach. Comput. Intell. 11(2), 323–337 (1995). CrossRefGoogle Scholar
  21. 21.
    Y.H. Cheng, C. Shih, S.C. Chiang et al., Introducing PCTRAN as an evaluation tool for nuclear power plant emergency responses. Ann. Nuclear Energy 40(1), 122–129 (2012). CrossRefGoogle Scholar

Copyright information

© China Science Publishing & Media Ltd. (Science Press), Shanghai Institute of Applied Physics, the Chinese Academy of Sciences, Chinese Nuclear Society and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yong-Kuo Liu
    • 1
    • 2
    Email author
  • Xin Ai
    • 2
  • Abiodun Ayodeji
    • 2
  • Mao-Pu Wu
    • 3
  • Min-Jun Peng
    • 2
  • Hong Xia
    • 2
  • Wei-Feng Yu
    • 2
  1. 1.State Key Laboratory of Nuclear Power Safety Monitoring Technology and EquipmentShenzhenChina
  2. 2.Fundamental Science on Nuclear Safety and Simulation Technology LaboratoryHarbin Engineering UniversityHarbinChina
  3. 3.Lianyungang JARI Deepsoft Technology Co., Ltd.LianyungangChina

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