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A Monte Carlo simulation approach to the availability assessment of multi-state systems with operational dependencies

Author

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  • Zio, E.
  • Marella, M.
  • Podofillini, L.

Abstract

This paper deals with multi-state systems (MSS), whose performance can settle on different levels, e.g. 100%, 80%, 50% of the nominal capacity, depending on the operative conditions of the constitutive multi-state elements. Examples are manufacturing, production, power generation and gas and oil transportation systems. Often in practice, MSS are such that operational dependencies exist between the system state and the state of its components. For example, in a production line of nodal series structure, with no buffers between the nodes, if one of the nodes throughput changes (e.g. switches from 100% to 50% due to a deterministic or stochastic transition of one of its components), the other nodes must be reconfigured (i.e. their components must deterministically change their states) so as to provide the same throughput.

Suggested Citation

  • Zio, E. & Marella, M. & Podofillini, L., 2007. "A Monte Carlo simulation approach to the availability assessment of multi-state systems with operational dependencies," Reliability Engineering and System Safety, Elsevier, vol. 92(7), pages 871-882.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:7:p:871-882
    DOI: 10.1016/j.ress.2006.04.024
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    Citations

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    Cited by:

    1. Jafary, Bentolhoda & Fiondella, Lance, 2016. "A universal generating function-based multi-state system performance model subject to correlated failures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 16-27.
    2. Hui Xiao & Minhao Cao & Gang Kou & Xiaojun Yuan, 2021. "Optimal element allocation and sequencing of multi-state series systems with two levels of performance sharing," Journal of Risk and Reliability, , vol. 235(2), pages 282-292, April.
    3. Esi Saari & Jing Lin & Liangwei Zhang & Bin Liu, 2019. "System availability assessment using a parametric Bayesian approach: a case study of balling drums," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 739-745, August.
    4. Mabel, M. Carolin & Raj, R. Edwin & Fernandez, E., 2010. "Adequacy evaluation of wind power generation systems," Energy, Elsevier, vol. 35(12), pages 5217-5222.
    5. Girish Kumar & Vipul Jain & Umang Soni, 2019. "Modelling and simulation of repairable mechanical systems reliability and availability," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1221-1233, October.
    6. Joanna Akrouche & Mohamed Sallak & Eric Châtelet & Fahed Abdallah & Hiba Hajj Chehade, 2022. "Methodology for the Assessment of Imprecise Multi-State System Availability," Mathematics, MDPI, vol. 10(1), pages 1-25, January.
    7. Li, Yan-Fu & Zio, Enrico, 2012. "A multi-state model for the reliability assessment of a distributed generation system via universal generating function," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 28-36.
    8. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    9. Noh, Yeelyong & Chang, Kwangpil & Seo, Yutaek & Chang, Daejun, 2014. "Risk-based determination of design pressure of LNG fuel storage tanks based on dynamic process simulation combined with Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 76-82.
    10. Cavalieri, Francesco, 2020. "Seismic risk assessment of natural gas networks with steady-state flow computation," International Journal of Critical Infrastructure Protection, Elsevier, vol. 28(C).
    11. Wu, Bei & Cui, Lirong & Fang, Chen, 2020. "Multi-state balanced systems with multiple failure criteria," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    12. Song, Xiaogang & Zhai, Zhengjun & Liu, Yidong & Han, Jie, 2018. "A stochastic approach for the reliability evaluation of multi-state systems with dependent components," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 257-266.
    13. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2017. "Optimal loading of series parallel systems with arbitrary element time-to-failure and time-to-repair distributions," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 34-44.
    14. Su, Peng & Wang, Guanjun & Duan, Fengjun, 2020. "Reliability evaluation of a k-out-of-n(G)-subsystem based multi-state system with common bus performance sharing," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    15. Durga Rao, K. & Gopika, V. & Sanyasi Rao, V.V.S. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2009. "Dynamic fault tree analysis using Monte Carlo simulation in probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 872-883.

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