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Reliability evaluation of non-reparable three-state systems using Markov model and its comparison with the UGF and the recursive methods

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  • Pourkarim Guilani, Pedram
  • Sharifi, Mani
  • Niaki, S.T.A.
  • Zaretalab, Arash

Abstract

In multi-state systems (MSS) reliability problems, it is assumed that the components of each subsystem have different performance rates with certain probabilities. This leads into extensive computational efforts involved in using the commonly employed universal generation function (UGF) and the recursive algorithm to obtain reliability of systems consisting of a large number of components. This research deals with evaluating non-repairable three-state systems reliability and proposes a novel method based on a Markov process for which an appropriate state definition is provided. It is shown that solving the derived differential equations significantly reduces the computational time compared to the UGF and the recursive algorithm.

Suggested Citation

  • Pourkarim Guilani, Pedram & Sharifi, Mani & Niaki, S.T.A. & Zaretalab, Arash, 2014. "Reliability evaluation of non-reparable three-state systems using Markov model and its comparison with the UGF and the recursive methods," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 29-35.
    • Handle: RePEc:eee:reensy:v:129:y:2014:i:c:p:29-35
    DOI: 10.1016/j.ress.2014.04.019
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    References listed on IDEAS

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    1. Li, Wei & Zuo, Ming J., 2008. "Reliability evaluation of multi-state weighted k-out-of-n systems," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 160-167.
    2. Ouzineb, Mohamed & Nourelfath, Mustapha & Gendreau, Michel, 2008. "Tabu search for the redundancy allocation problem of homogenous series–parallel multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1257-1272.
    3. Ushakov, Igor, 2000. "The method of generalized generating sequences," European Journal of Operational Research, Elsevier, vol. 125(2), pages 316-323, September.
    4. Tian, Zhigang & Levitin, Gregory & Zuo, Ming J., 2009. "A joint reliability–redundancy optimization approach for multi-state series–parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1568-1576.
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    Cited by:

    1. Guilani, Pardis Pourkarim & Ardakan, Mostafa Abouei & Dobani, Ehsan Ramezani, 2022. "Optimal component sequence in heterogeneous 1-out-of-N mixed RRAPs," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Zaretalab, Arash & Sharifi, Mani & Guilani, Pedram Pourkarim & Taghipour, Sharareh & Niaki, Seyed Taghi Akhavan, 2022. "A multi-objective model for optimizing the redundancy allocation, component supplier selection, and reliable activities for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Zhou, Taotao & Zhang, Xiaoge & Droguett, Enrique Lopez & Mosleh, Ali, 2023. "A generic physics-informed neural network-based framework for reliability assessment of multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Sharifi, Mani & Taghipour, Sharareh, 2024. "Redundancy allocation problem with a mix of components for a multi-state system and continuous performance level components," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Zhang, Hanxiao & Sun, Muxia & Li, Yan-Fu, 2022. "Reliability–redundancy allocation problem in multi-state flow network: Minimal cut-based approximation scheme," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Ruiz-Castro, Juan Eloy, 2016. "Markov counting and reward processes for analysing the performance of a complex system subject to random inspections," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 155-168.
    7. Zaretalab, Arash & Hajipour, Vahid & Tavana, Madjid, 2020. "Redundancy allocation problem with multi-state component systems and reliable supplier selection," Reliability Engineering and System Safety, Elsevier, vol. 193(C).

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