Analysis of Related Factors Influencing Reliability of Railway Signaling Systems Based on Fuzzy Analytical Hierarchy Process

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 288)

Abstract

A signaling system is a safety-critical part of railways. Most of the railway signaling systems currently used today are intelligent and automatic high-performance systems, for which the high level of reliability is required. However, there are many factors including those uncertainties that affect system reliability performance. In this paper, fuzzy analytical hierarchy process is further developed by using hierarchical structure to determine weights of contributions of each factor and subfactor to the reliability performance of the system. By using the proposed methodology, the results of reliability analysis indicate that the reliability of a signaling system can be assessed effectively and efficiently.

Keywords

Railway signaling system Fuzzy analytic hierarchy process Influencing factor Reliability 

References

  1. 1.
    Zhou L, Shen Z (2011) Progress in high-speed train technology around the world. J Mod Transp 19(1):1–6CrossRefGoogle Scholar
  2. 2.
    An M, Chen Y, Baker CJ (2011) A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: a railway risk management system. Inf Sci 181:3946–3966CrossRefGoogle Scholar
  3. 3.
    Miee CE. EMC issues in advanced signaling transmission based train control systems. The IET, Michael Faraday House, Six Hills Way, Stevenage, Herts SG1 2AY, UKGoogle Scholar
  4. 4.
    Zhang G, Zhu N, Tian Z, Chen Y, Sun B (2012). Application of trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Saf Sci 50:228–239CrossRefGoogle Scholar
  5. 5.
    An M, Lin W, Stirling A (2006) Fuzzy-based-approach to qualitative railway risk assessment. Proc IMechE Part F: J Rail Rapid Transit 220:153–167Google Scholar
  6. 6.
    Ardente F, Beccali M, Cellura M (2004) A fuzzy software for the energy and environmental balances of products. Ecol Model 176:359–379CrossRefGoogle Scholar
  7. 7.
    Zadeh LA (1965) Fuzzy Sets. Inf Control 8:338–353MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Timothy RJ (2010) Fuzzy logic with engineering applications (3rd ed). WileyGoogle Scholar
  9. 9.
    Tesfamariam S, Sadiq R (2006) Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP). Stoch Environ Res Risk Assess 21:35–50Google Scholar
  10. 10.
    Chang T, Wang TC (2009) Using the fuzzy multi-criteria decision making approach for measuring the possibility of successful knowledge management. Inf Sci 179(4):355–370CrossRefGoogle Scholar
  11. 11.
    Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95:649–655CrossRefMATHGoogle Scholar
  12. 12.
    Veerabathiran R (2012) Applications of the extent analysis method on fuzzy AHP. Int J Eng Sci Technol 4(7):3472–3480Google Scholar
  13. 13.
    Vernez D, Vuille F (2009) A method to assess and optimise dependability of complex macro-systems: application to a railway signalling system. Saf Sci 47:382–394CrossRefGoogle Scholar
  14. 14.
    EN 50129 (2002) Railway applications: communication, signaling and processing systems—safety related electronic systems for signalingGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Nanjing Institute of Railway TechnologyNanjingChina
  2. 2.School of Civil EngineeringUniversity of BirminghamBirminghamUK

Personalised recommendations