Advertisement

Train Reliability and Safety Analysis

  • Yong Qin
  • Limin Jia
Chapter
Part of the Advances in High-speed Rail Technology book series (ADVHIGHSPEED)

Abstract

In this chapter, the authors research on the train reliability and safety analysis. Firstly, safety and reliability standards and procedures are introduced. Those standards and procedures provide a guideline for the train reliability and safety analysis. Then, reliability analysis and prediction of bogie frame is carried out to ensure the train safety operation. Survival analysis and heuristic algorithms are employed in this study. More specifically, residual life prediction of rolling bearings with the harsh operating conditions, complex structure and sophisticated mechanism is researched using GA-BP. Finally, the authors construct the index system of high speed train and the train operational risk is assessed based on dynamic VIKOR in high speed. Field examples are listed to verify the effectiveness of those proposed methods.

References

  1. 1.
    S.H. Baek, S.S. Cho, W.S. Joo, Fatigue life prediction based on the rainflow cycle counting method for the end beam of a freight car bogie. Int. J. Automot. Technol. 9(1), 95–101 (2008)CrossRefGoogle Scholar
  2. 2.
    Y. Lu et al., Reliability and parametric sensitivity analysis of railway vehicle bogie frame based on monte-carlo numerical simulation (2010)Google Scholar
  3. 3.
    S.G. Zhang, Study on testing and establishment method for the load spectrum of bogie frame for high-speed trains. Sci. China 51(12), 2142–2151 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    X. Wang, X. Li, F. Li, Analysis on oscillation in electro-hydraulic regulating system of steam turbine and fault diagnosis based on PSOBP. Expert Syst. Appl. 37(5), 3887–3892 (2010)CrossRefGoogle Scholar
  5. 5.
    B. Yazici, S. Yolacan, A comparison of various tests of normality. J. Stat. Comput. Simul. 77(2), 175–183 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    J.F. Lawless, Statistical Models and Methods for Lifetime Data (Wiley-Interscience, New York, 2003), pp. 264–265zbMATHGoogle Scholar
  7. 7.
    A. Barabadi, J. Barabady, T. Markeset, Maintainability analysis considering time-dependent and time-independent covariates. Reliab. Eng. Syst. Saf. 96(1), 210–217 (2011)CrossRefGoogle Scholar
  8. 8.
    M. Guo et al., The impact of personality on driving safety among Chinese high-speed railway drivers. Accid. Anal. Prev. 92, 9–14 (2016)CrossRefGoogle Scholar
  9. 9.
    G. Gou et al., Effect of humidity on porosity, microstructure, and fatigue strength of A7N01S-T5 aluminum alloy welded joints in high-speed trains. Mater. Des. 85, 309–317 (2015)CrossRefGoogle Scholar
  10. 10.
    C.F. Hung, W.L. Hsu, Influence of long-wavelength track irregularities on the motion of a high-speed train. Veh. Syst. Dyn. 12, 1–18 (2017)Google Scholar
  11. 11.
    E. Jafarian, M.A. Rezvani, Application of fuzzy fault tree analysis for evaluation of railway safety risks: An evaluation of root causes for passenger train derailment. Proc. Inst. Mech. Eng. F J. Rail Rapid Transp. 226(1), 14–25 (2012)CrossRefGoogle Scholar
  12. 12.
    G. Bearfield, W. Marsh, Generalising event trees using bayesian networks with a case study of train derailment. Lect. Notes Comput. Sci 3688, 52–66 (2005)CrossRefGoogle Scholar
  13. 13.
    P. Antonio, R. Fabrizio, A. Raffaele, Bayesian analysis and prediction of failures in underground trains. Qual. Reliab. Eng. Int. 19(4), 327–336 (2010)Google Scholar
  14. 14.
    W.J. Zhang, N. Lan, Research on the reliability growth management techniques of high-speed train for whole life cycle (2013)CrossRefGoogle Scholar
  15. 15.
    Y. Wang et al., Research on design evaluation of high-speed train auxiliary power supply system based on the AHP, in Transportation Electrification Asia-Pacific (2014)Google Scholar
  16. 16.
    S. Opricovic, G.H. Tzeng, Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156(2), 445–455 (2004)CrossRefGoogle Scholar
  17. 17.
    S. Opricovic, G.H. Tzeng, Extended VIKOR method in comparison with outranking methods. Eur. J. Oper. Res. 178(2), 514–529 (2007)CrossRefGoogle Scholar
  18. 18.
    A. Jahan, K.L. Edwards, VIKOR method for material selection problems with interval numbers and target-based criteria. Mater. Des. 47(47), 759–765 (2013)CrossRefGoogle Scholar
  19. 19.
    K. Devi, Extension of VIKOR method in intuitionistic fuzzy environment for robot selection. Expert Syst. Appl. 38(11), 14163–14168 (2011)Google Scholar
  20. 20.
    O. Mohsen, N. Fereshteh, An extended VIKOR method based on entropy measure for the failure modes risk assessment – A case study of the geothermal power plant (GPP). Saf. Sci. 92, 160–172 (2017)CrossRefGoogle Scholar
  21. 21.
    L.A. Zadeh, Fuzzy sets, information and control. Inf. Control. 8(3), 338–353 (1965)CrossRefGoogle Scholar
  22. 22.
    K.T. Atanassov, Remarks on the intuitionistic fuzzy sets. Fuzzy Sets Syst. 33(1), 37–45 (1989)MathSciNetCrossRefGoogle Scholar
  23. 23.
    F. Shen et al., An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation. Inf. Sci. (2017)Google Scholar
  24. 24.
    L.E. Wang, H.C. Liu, M.Y. Quan, Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments (Pergamon Press, 2016), pp. 175–185Google Scholar
  25. 25.
    S.P. Wan, F. Wang, J.Y. Dong, A novel risk attitudinal ranking method for intuitionistic fuzzy values and application to MADM (Elsevier Science Publishers B. V., 2016), pp. 98–112Google Scholar
  26. 26.
    C. Yue, A geometric approach for ranking interval-valued intuitionistic fuzzy numbers with an application to group decision-making. Comput. Ind. Eng. 102, 233–245 (2016)CrossRefGoogle Scholar
  27. 27.
    G. Kumar, R.K. Bajaj, N. Gandotra, Algorithm for shortest path problem in a network with interval-valued intuitionistic trapezoidal fuzzy number. Procedia Comput. Sci. 70, 123–129 (2015)CrossRefGoogle Scholar
  28. 28.
    S. Guo, W. Yin, Multiple attribute decision making method based on 2-type intuitionistic fuzzy information. Fuzzy Syst. Math. 27(3), 128–133 (2013)MathSciNetzbMATHGoogle Scholar
  29. 29.
    F. Liu, X.H. Yuan, Fuzzy number intuitionistic fuzzy set. Fuzzy Syst. Math. 21(1), 88–91 (2007)MathSciNetzbMATHGoogle Scholar
  30. 30.
    X.U. Danqing, X. Chen, D.O. Mathematics, multi-attribute decision-making method based on hesitant intuitionistic fuzzy linguistic set. J, in Huaibei Normal Univ, (2016)Google Scholar
  31. 31.
    B. Zhou, M. Xie, W.U. Keming, Analysis and prediction on the current situation of the repair class and repair system of electric multiple units(EMU). Electric Drive for Locomotives (2017)Google Scholar
  32. 32.
    Y. Fu, et al., Operation safety assessment of high-speed train with fuzzy group decision making method and empirical research, in International Conference on Cloud Computing and Internet of Things (2017)Google Scholar
  33. 33.
    Y. Yang, Y. Liu, M. Zhou, F. Li, C. Sun, Robustness assessment of urban rail transit based on complex network theory: A case study of the Beijing Subway. Saf. Sci. 79, 149–162 (2015)CrossRefGoogle Scholar
  34. 34.
    A.M. Sarhan, Reliability estimations of components from masked system life data. Reliab. Eng. Syst. Saf. 74(1), 107–113 (2001)CrossRefGoogle Scholar
  35. 35.
    M. Macchi, M. Garetti, D. Centrone, L. Fumagalli, G.P. Pavirani, Maintenance management of railway infrastructures based on reliability analysis. Reliab. Eng. Syst. Saf. 104, 71–83 (2012)CrossRefGoogle Scholar
  36. 36.
    J. Lin, J. Pulido, M. Asplund, Reliability analysis for preventive maintenance based on classical and Bayesian semi-parametric degradation approaches using locomotive wheel-sets as a case study. Reliab. Eng. Syst. Saf. 134, 143–156 (2015)CrossRefGoogle Scholar
  37. 37.
    M. Kurant, P. Thiran, Layered complex networks. Phys. Rev. Lett. 96(13) (2006)Google Scholar
  38. 38.
    V.Y. Guleva, M.V. Skvorcova, A.V. Boukhanovsky, Using multiplex networks for banking systems dynamics modelling. Procedia Comput. Sci. 66, 257–266 (2015)CrossRefGoogle Scholar
  39. 39.
    R. Mittal, M.P.S. Bhatia, Anomaly detection in multiplex networks. Procedia Comput. Sci. 125, 609–616 (2018)CrossRefGoogle Scholar
  40. 40.
    S. Opricovic, G.-H. Tzeng, Extended VIKOR method in comparison with outranking methods. Eur. J. Oper. Res. 178(2), 514–529 (2007)CrossRefGoogle Scholar
  41. 41.
    L. Zhang, W. Dong, D. Zhang, G. Shi, Two-stage image denoising by principal component analysis with local pixel grouping. Patt. Rec. 43(4), 1531–1549 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yong Qin
    • 1
  • Limin Jia
    • 1
  1. 1.Beijing Jiaotong UniversityBeijingChina

Personalised recommendations