Application of DBN for Assessment of Railway Intelligent Signal System Reliability

  • Zhengjiao Li
  • Bai-gen Cai
  • Shaobin Li
  • Jiang Liu
  • Debiao Lu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 482)


According to the variable structure characteristics of railway intelligent signal system (RISS) with different railway station scale, a new reliability assessment method based on Dynamic Bayesian Networks (DBN) is studied. A comparison between DBN model and probabilistic model is studied to verify the accuracy and correctness of DBN model. Based on DBN model, the static gates analyzing results deliver a calculation with no error, while the spare gate analyzing results deliver a calculation with a tolerable error that leads to more strictly and credible calculations. Meanwhile, this paper analyzes reliability indexes of RISS with four different railway station scale. The results show that: when the railway station scale increases, the reliability of RISS decreases, which has little impact on the ranking of the components’ Birnbaum importance factor and diagnostic importance factor.


Railway intelligent signal system Dynamic bayesian networks Reliability assessment 



This work was supported by National Natural Science Foundation of China under Grant No. 61490705, National Natural Science Foundation of China under Grant No. 61603027 and the Fundamental Research Funds for the Central Universities under Grant 2015JBM012.


  1. 1.
    Kuang W (2012) Distributed computer interlocking system of railway station. China Railway Sci 33(5):139–143Google Scholar
  2. 2.
    Li S, Li W (2015) Design and implementation of a novel intelligent signal control system for railway. Modern Electron Tech 38(14):156–159MathSciNetGoogle Scholar
  3. 3.
    Liu J, Guo H et al (2015) Research on reliability of auxiliary power system of CRH3 electric multiple units. J China Railway Soc 37(11):44–51Google Scholar
  4. 4.
    Shang G, Yuan M et al (2016) Design of and performance evaluation methods for GNSS-based train positioning unit. J China Railway Soc 38(2):64–73Google Scholar
  5. 5.
    Wang H, Lu Z, Zhang B (2012) Analysis method for the operational reliability of emu running gear based on Fault tree and Bayesian network. China Railway Sci 33(8):60–64Google Scholar
  6. 6.
    Su H, Che Y, Zhang Y (2014) Dependability assessment of CTCS-3 on-board subsystem based on bayesian network. China Railway Sci 35(5):96–104Google Scholar
  7. 7.
    Xue Feng, Wang X (2011) Analysis on reliability and performance of computer-based interlocking system with the dynamic fault tree method. J China Railway Soc 33(12):78–82Google Scholar
  8. 8.
    Tan X, He Z et al (2011) Analysis on reliability of the subway station-level integrated supervisory and control system based on dynamic fault tree analysis. J China Railway Soc 33(7):52–60Google Scholar
  9. 9.
    Ruijters E, Stoelinga M (2015) Fault tree analysis: a survey of the state-of-the-art in modeling, analysis and tools. Comput Sci Rev 15:29–62MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Montani S, Portinale L, Bobbio A (2005) Dynamic Bayesian networks for modeling advanced fault tree features in dependability analysis. In: Proceedings of the 16th european conference on safety and reliability, Leiden, The Netherlands, AA Balkema, pp 1415–1422Google Scholar
  11. 11.
    Zhuo Z, Ma Z et al (2008) Dynamic fault tree analysis based on dynamic Bayesian networks. Syst Eng-Theory Pract 28(2):35–42Google Scholar
  12. 12.
    Liu D, Zhang H et al (2013) Methodologies of dynamic fault trees analysis. National Defense Industry Press, BeijingGoogle Scholar
  13. 13.
    Duan R, Fan J (2014) Reliability evaluation of data communication system based on dynamic fault tree under epistemic uncertainty. Math Problems EngGoogle Scholar
  14. 14.
    Su H (2013) Reliability and security analysis on two-cell dynamic redundant system. Indonesian J Electr Eng Comput Sci 11(5):2594–2604Google Scholar
  15. 15.
    Xia J, Zhang C, Bai R et al (2013) Real-time and reliability analysis of time-triggered CAN-bus. Chin J Aeronaut 26(1):171–178CrossRefGoogle Scholar
  16. 16.
    Zhou D (2014) The application of bayesian networks in system reliability. Arizona State UniversityGoogle Scholar
  17. 17.
    Murphy K (2001) The bayes net toolbox for matlab. Comput Sci Statistics 33(2):1024–1034Google Scholar
  18. 18.
    Cheshmikhani E, Zarandi HR (2015) Probabilistic analysis of dynamic and temporal fault trees using accurate stochastic logic gates. Microelectron Reliab 55(11):2468–2480CrossRefGoogle Scholar
  19. 19.
    Marquez D, Neil M, Fenton N (2010) Improved reliability modeling using Bayesian networks and dynamic discretization. Reliability Eng Syst Safety 95(4):412–425CrossRefGoogle Scholar
  20. 20.
    Agena Ltd (2016) AgenaRisk software package,
  21. 21.
    ELSAYED A (2013). Reliability engineering (Second Edition). (trans: Zhou Y). Electronic Industry Press, Beijing, pp 98–106Google Scholar
  22. 22.
    Tao Y, Dong D, Ren P (2010) An improved method for system fault diagnosis using fault tree analysis. J Harbin Institute Technol 1:143–147Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Zhengjiao Li
    • 1
  • Bai-gen Cai
    • 1
  • Shaobin Li
    • 1
  • Jiang Liu
    • 1
  • Debiao Lu
    • 1
  1. 1.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina

Personalised recommendations