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Method for evaluating an extended Fault Tree to analyse the dependability of complex systems: Application to a satellite-based railway system

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  • Nguyen, T.P. Khanh
  • Beugin, Julie
  • Marais, Juliette

Abstract

Evaluating dependability of complex systems requires the evolution of the system states over time to be analysed. The problem is to develop modelling approaches that take adequately the evolution of the different operating and failed states of the system components into account. The Fault Tree (FT) is a well-known method that efficiently analyse the failure causes of a system and serves for reliability and availability evaluations. As FT is not adapted to dynamic systems with repairable multi-state components, extensions of FT (eFT) have been developed. However efficient quantitative evaluation processes of eFT are missing. Petri nets have the advantage of allowing such evaluation but their construction is difficult to manage and their simulation performances are unsatisfactory. Therefore, we propose in this paper a new powerful process to analyse quantitatively eFT. This is based on the use of PN method, which relies on the failed states highlighted by the eFT, combined with a new analytical modelling approach for critical events that depend on time duration. The performances of the new process are demonstrated through a theoretical example of eFT and the practical use of the method is shown on a satellite-based railway system.

Suggested Citation

  • Nguyen, T.P. Khanh & Beugin, Julie & Marais, Juliette, 2015. "Method for evaluating an extended Fault Tree to analyse the dependability of complex systems: Application to a satellite-based railway system," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 300-313.
    • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:300-313
    DOI: 10.1016/j.ress.2014.09.019
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    References listed on IDEAS

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    Cited by:

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    2. Wu, Daohua & Zheng, Wei, 2018. "Formal model-based quantitative safety analysis using timed Coloured Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 62-79.
    3. Daniel (Jian) Sun & Yuhan Zhao & Qing-Chang Lu, 2015. "Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China," Sustainability, MDPI, vol. 7(6), pages 1-18, May.
    4. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    5. Xingchuan Wang & Enjian Yao & Shasha Liu, 2018. "Travel Choice Analysis under Metro Emergency Context: Utility? Regret? Or Both?," Sustainability, MDPI, vol. 10(11), pages 1-15, October.
    6. Yu Zang & Wei Shangguan & Baigen Cai & Huashen Wang & Michael G Pecht, 2019. "Methods for fault diagnosis of high-speed railways: A review," Journal of Risk and Reliability, , vol. 233(5), pages 908-922, October.
    7. Son, Kwang Seop & Seong, Seung Hwan & Kang, Hyun Gook & Jang, Gwi Sook, 2020. "Development of state-based integrated dependability model of RPS in NPPs considering CCF and periodic testing effects at the early design phase," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    8. Khaled Guerraiche & Latifa Dekhici & Eric Chatelet & Abdelkader Zeblah, 2021. "Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm," Energies, MDPI, vol. 14(13), pages 1-19, July.

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