Operation-Oriented Reliability and Availability Evaluation for Onboard High-Speed Train Control System with Dynamic Bayesian Network

  • Baoping CaiEmail author
  • Yonghong Liu
  • Zengkai Liu
  • Yuanjiang Chang
  • Lei Jiang


The reliability and availability of the onboard high-speed train control system are important to guarantee operational efficiency and railway safety. Failures occurring in the onboard system may result in serious accidents. In the analysis of the effects of failure, it is significant to consider the operation of an onboard system. This paper presents a systemic approach to evaluate the reliability and availability for the onboard system based on dynamic Bayesian network, with taking into account dynamic failure behaviors, imperfect coverage factors, and temporal effects in operational phase. The case studies are presented and compared for onboard systems with different redundant strategies, i.e., the triple modular redundancy, hot spare double dual, and cold spare double dual. Dynamic fault trees of the three kinds of onboard system are constructed and mapped into dynamic Bayesian network. The forward and backward inferences are conducted not only to evaluate the reliability and availability, but also recognize the vulnerabilities of the onboard systems. A sensitivity analysis is carried out for evaluating the effects of failure rates subject to uncertainties. To improve the reliability and availability, the recovery mechanism should be paid more attention. Finally, the proposed approach is validated with the field data from one railway bureau in China and some industrial impacts are provided.


Operation-oriented Reliability Availability Train control system Dynamic bayesian network 


  1. 1.
    UNISIG. Subset-026 of the ERTMS/ETCS system requirements specification (SRS), 2012Google Scholar
  2. 2.
    CTCS general Rules of technical specification, Ministry of Railways (Science and Technology Division, Beijing, China, 2004)Google Scholar
  3. 3.
    J.B. Dugan, S.J. Bavuso, M.A. Boyd, Dynamic fault-tree models for fault-tolerant computer systems. IEEE Trans. Reliab. 41(3), 363–377 (1992)CrossRefGoogle Scholar
  4. 4.
    H. Langseth, L. Portinale, Bayesian networks in reliability. Reliab. Eng. Syst. Saf. 92, 92–108 (2007)CrossRefGoogle Scholar
  5. 5.
    F. Flammini, S. Marrone, N. Mazzocca et al., Modeling system reliability aspects of ERTMS/ETCS by fault trees and Bayesian Network, in Safety and Reliability for Managing Risk, ed. by G. Soares, Zio (Taylor & Francis Group, London, 2006), pp. 2675–2683Google Scholar
  6. 6.
    F. Flammini, S. Marrone, M Iacono et al., A multi-formalism modular approach to ERTMS/ETCS failure modeling. Int. J. Reliab. Qual. Safety Eng. 21(1):1450001-1–1450001-29 (2014)CrossRefGoogle Scholar
  7. 7.
    L.Q. Di, X. Yuan, Y.N. Wang, Research on the evaluation method for the RAM goals of CTCS-3. China Railway Sci. 31(6), 92–97 (2010)Google Scholar
  8. 8.
    H.S. Su, Y.L. Che, Dependability assessment of CTCS-3 on-board subsystem based on Bayesian network. China Railway Sci. 35(5), 96–104 (2014)Google Scholar
  9. 9.
    S. Qiu, M. Sallak, W. Schön et al., Availability assessment of railway signalling systems with uncertainty analysis using Statecharts. Simul. Modell. Pract. Theory 47, 1–18 (2014)CrossRefGoogle Scholar
  10. 10.
    S. Qiu, M. Sallak, W. Schön et al., Modeling of ERTMS level 2 as an SoS and evaluation of its dependability parameters using state charts. IEEE Syst. J. 8 (4), 1169–1181 (2014)CrossRefGoogle Scholar
  11. 11.
    S. Bernardi, F. Flammini, S. Marrone et al. Model-driven availability evaluation of railway control systems, in International Conference on Computer Safety, Reliability, and Security, Naples, Italy, 19–22 Sept 2011 (Springer, Berlin), pp. 15–28Google Scholar
  12. 12.
    L. Carnevali, F. Flammini, M. Paolieri et al., Non-markovian performability evaluation of ERTMS/ETCS Level 3, in European Workshop on Computer Performance Engineering, Madrid, Spain, 31 Aug–1 Sept 2015 (Springer, Cham), pp. 47–62CrossRefGoogle Scholar
  13. 13.
    M. Biagi, L. Carnevali, M. Paolieri et al., Performability evaluation of the ERTMS/ETCS-Level 3. Transp. Res. Part C: Emerg. 82, 314–336 (2014)CrossRefGoogle Scholar
  14. 14.
    G. Neglia, S. Alouf, A. Dandoush et al., Performance evaluation of train moving-block control, in International Conference on Quantitative Evaluation of Systems, Quebec City, Canada, 23–25 Aug 2016 (Springer, Cham), pp. 348–363CrossRefGoogle Scholar
  15. 15.
    P. Weber, G. Medina-Oliva, C. Simon et al., Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Eng. Appl. Artif. Intell. 25: 671–682 (2012)CrossRefGoogle Scholar
  16. 16.
    B.P. Cai, L. Huang, M. Xie, Bayesian networks in fault diagnosis. IEEE Trans. Ind. Inf. 13(5), 2227–2240 (2017)CrossRefGoogle Scholar
  17. 17.
    D. Codetta-Raiteri, L. Portinale, Approaching dynamic reliability with predictive and diagnostic purposes by exploiting dynamic Bayesian networks. Proc. IMechE Part O: J. Risk and Reliab. 228(5), 488–503 (2014)Google Scholar
  18. 18.
    H. Boudali, J.B. Dugan, A discrete-time Bayesian network reliability modeling and analysis framework. Reliab. Eng. Syst. Saf. 87, 337–349 (2005)CrossRefGoogle Scholar
  19. 19.
    B.P. Cai, H.L. Liu, M. Xie, A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks. Mech. Syst. Sig. Process 80, 31–44 (2016)CrossRefGoogle Scholar
  20. 20.
    M. Neil, D. Marquez, Availability modelling of repairable systems using Bayesian networks. Eng. Appl. Artif. Intell. 25, 698–704 (2012)CrossRefGoogle Scholar
  21. 21.
    X.F. Liang, H.D. Wang, H. Yi et al. Warship reliability evaluation based on dynamic bayesian networks and numerical simulation. Ocean Eng. 136, 129–140 (2017)CrossRefGoogle Scholar
  22. 22.
    B.P. Cai, Y.H. Liu, Y.W. Zhang et al. Dynamic Bayesian networks based performance evaluation of subsea blowout preventers in presence of imperfect repair. Expert Syst. Appl. 40, 7544–7554 (2013)CrossRefGoogle Scholar
  23. 23.
    B.P. Cai, M. Xie, Y.H. Liu et al. Availability-based engineering resilience metric and its corresponding evaluation methodology. Reliab. Eng. Syst. Saf. 172, 216–224 (2018)CrossRefGoogle Scholar
  24. 24.
    A. Bobbio, L. Portinale, M. Minichino et al. Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliab. Eng. Syst. Saf. 249–260 (2001)CrossRefGoogle Scholar
  25. 25.
    L. Portinale, D. Codetta-Raiteri, S. Montani, Supporting reliability engineers in exploiting the power of dynamic Bayesian networks. Int. J. Approximate Reasoning 51, 179–195 (2010)CrossRefGoogle Scholar
  26. 26.
    S. Montani, L. Portinale, A. Bobbio et al. RADYBAN: a tool for reliability analysis of dynamic fault trees through conversion into dynamic Bayesian networks. Reliab. Eng. Syst. Saf. 93, 922–932 (2008)CrossRefGoogle Scholar
  27. 27.
    J.Y. Zhu, A. Deshmukh, Application of Bayesian decision networks to life cycle engineering in Green design and manufacturing. Eng. Appl. Artif. Intell. 16, 91–103 (2003)CrossRefGoogle Scholar
  28. 28.
    A.G. Wilsona, A.V. Huzurbazar, Bayesian networks for multilevel system reliability. Reliab. Eng. Syst. Saf. 92(10), 1413–1420 (2007)CrossRefGoogle Scholar
  29. 29.
    K.P. Murphy, Dynamic Bayesian Networks: representation (Inference and Learning. University of California, Berkeley, 2002)Google Scholar
  30. 30.
    J.B. Dugan, K. Trivedi, Coverage modeling for dependability analysis of fault-tolerant systems. IEEE Trans. Comput. 38, 775–787 (1989)CrossRefGoogle Scholar
  31. 31.
    B. Jones, I. Jenkinson, Z. Yang et al. The use of Bayesian network modelling for maintenance planning in a manufactoring industy. Reliab. Eng. Syst. Saf. 95, 267–277 (2010)CrossRefGoogle Scholar
  32. 32.
    CTCS-3 system requirements specification. Ministry of Railways, Science and Technology Division, Beijing, China (2008)Google Scholar
  33. 33.
    CTCS-3 function requirements specification. Ministry of Railways, Science and Technology Division, Beijing, China (2008)Google Scholar
  34. 34.
    University of Pittsburgh, GeNIe and SMILE-Home.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Baoping Cai
    • 1
    Email author
  • Yonghong Liu
    • 1
  • Zengkai Liu
    • 1
  • Yuanjiang Chang
    • 1
  • Lei Jiang
    • 2
  1. 1.China University of PetroleumQingdaoChina
  2. 2.Southwest Jiaotong UniversityChengduChina

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