Reliability Analysis of On-Board ATP System in Metro Based on Fuzzy Dynamic Fault Tree

  • Jing WangEmail author
  • Tao He
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1060)


This paper proposes a reliability analysis method called fuzzy dynamic fault tree, which can improve the inadequacies of the traditional reliability analysis methods, such as dynamic characteristics unconsidered and accurate data not easy obtained. In this paper, the fuzzy number is used to indicate the failure rate distribution interval and the maintenance rate distribution interval when the unit fails. The dynamic fault tree is used to model the on-board ATP system, the hierarchical iteration method is used to divide the dynamic fault tree model into modules, and the Markov state transfer matrix method combined with the fuzzy number algorithm is finally used to obtain the reliability index distribution of the subway ATP system. The comparison between the obtained reliability index distribution and the results obtained by Isograph shows that fuzzy dynamic fault tree analysis can clearly describe the dynamic characteristics of the system, get higher precision reliability index and is more consistent with the actual situation because of considering repair-ability and the problem of accurate data being not easy to obtain. This method provides a new reference for the reliability analysis and evaluation of metro vehicle ATP system.


Dynamic fault tree Fuzzy number Markov matrix interaction method On-board ATP system Reliability 



This work was financially supported by the Natural Science Foundation of China, No. 51767014/2017, the Scientific and Technological Research and Development Program of the China Railway Corporation 2016X003-H.


  1. 1.
    Liu X, Zhang Y, Tang Z. Urban rail transit intelligent control system. Beijing: China Railway Press; 2008. p. 1–10.Google Scholar
  2. 2.
    Huang M. Study on functional safety analysis of CBTC on-board ATP system. Chengdu: Southwest Jiaotong University; 2014. p. 1–20.Google Scholar
  3. 3.
    Bingbing D, Baoqian D, Tong W. Dynamic fault tree analysis of on-board ATP system in metro based on Isograph. J Safety Sci Technol. 2016;12(5):80–5.Google Scholar
  4. 4.
    Xie B. Research of fault diagnosis expert system for ATO based on fault-tree. Lanzhou: Lanzhou Jiaotong University; 2013. p. 9–20.Google Scholar
  5. 5.
    Wu D. Human reliability analysis of subway train dispatching system based on the Bayesian Networks. Chengdu: Southwest Jiaotong University; 2018. p. 15–20.Google Scholar
  6. 6.
    Zhao Yang Xu, Tianhua Zhou Yuping, Wentian Zhao. Bayesian network based fault diagnosis system for vehicle on-board equipment of high-speed railway. J China Railw Soc. 2014;36(11):48–53.Google Scholar
  7. 7.
    Peters H, Materne RT, Notter M. Derivation of safety targets for the intermittent automatic train control. Signal und Draht. 2005;97(03):6–10.Google Scholar
  8. 8.
    Haizhu Hong, Zongfu Hu. Reliability of on-board ATP safety system based on BDD. J Transp Inf Saf. 2008;26(6):175–9.Google Scholar
  9. 9.
    Feifei Dong. Safety assessment on train control system of rail transportation based on Bayesian theory. Beijing: Beijing Jiaotong University; 2013.Google Scholar
  10. 10.
    Zhang W. Reliability analysis ATP of urban transit system and security strategy. Chengdu: Southwest Jiaotong University, School of Science & Technology; 2007. p. 29.Google Scholar
  11. 11.
    Xiaoping Xiong, Jiancheng Tan, Xiangning Lin. Reliability analysis of communication systems in substation based on dynamic fault tree. Proc CSEE. 2012;32(34):135–41.Google Scholar
  12. 12.
    Xue Feng, Fuxi Wang. Analysis on reliability and performance of computer-based interlocking system with the dynamic fault tree method. J China Railw Soc. 2011;33(12):78–82.Google Scholar
  13. 13.
    Xiaojie Zhang, Qiang Miao, Haitao Zhao. Reliability analysis of satellite system based on dynamic fault tree. J Astronaut. 2009;30(3):1249–55.Google Scholar
  14. 14.
    Xingyun Li, Jinping Qi. Reliability analysis of multi pantograph system based on fuzzy Bayesian network. J Railw Sci Eng. 2018;15(6):1384–9.Google Scholar
  15. 15.
    Fu J. Research of fault tree analysis based on fuzzy theory. Chengdu: Sichuan University; 2001. p. 18–20.Google Scholar
  16. 16.
    Huang H. A new fuzzy set approach to mechanical system fault tree analysis. Mech Sci Technol Aerosp Eng. 1994;1:1–7.Google Scholar
  17. 17.
    Ying Liu, Yangliang Xiao, Genbao Zhang, Yan Ran, Lizhang Li. Fault tree analysis of grinding wheel rack system of CNC grinder based on trapezoidal fuzzy number. Chinese J Eng Des. 2018;25(4):395–9.Google Scholar
  18. 18.
    Wang YC, Yu WX, Zhuang ZW. A study of fault tree analysis based on failure rate as fuzzy number. Syst Eng Theor Pract. 2000;20(12):102–7.Google Scholar
  19. 19.
    Mentes A, Helvacioglu I. An application of fuzzy fault tree analysis for spread mooring systems. Ocean Eng. 2011;38(2/3):285–94.CrossRefGoogle Scholar
  20. 20.
    Zhang Y. Application of the reliability theory and engineering technology. Lanzhou: Lanzhou University Press; 2003. p. 87–137.Google Scholar
  21. 21.
    Baosong Liang, Dianli Cao. Fuzzy mathematics foundation and application. Beijing: Sci Press; 2007. p. 111–6.Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Automation and Electrical EngineeringLanzhou Jiaotong UniversityLanzhouChina

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