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Probabilistic Availability Analysis for Marine Energy Transfer Subsystem Using Bayesian Network

Author

Listed:
  • Yi Yang

    (Department of The Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg Øst, Denmark)

  • John Dalsgaard Sørensen

    (Department of The Built Environment, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg Øst, Denmark)

Abstract

This research work proposes a novel approach to estimate probabilities of availability states of the energy transfer network in marine energy conversion subsystems, using Bayesian Networks (BNs). The logical interrelationships between units at different level in this network can be understood through qualitative system analysis, which then can be modeled by the fault tree (FT). The FT can be mapped to a corresponding BN, and the condition probabilities of nodes can be determined based on the logic structure. A case study was performed to demonstrate how the mapping is implemented, and the probabilities of availability states were estimated. The results give the probability of each availability state as a function of time, which serves as a basis for choosing the optimal design solution.

Suggested Citation

  • Yi Yang & John Dalsgaard Sørensen, 2020. "Probabilistic Availability Analysis for Marine Energy Transfer Subsystem Using Bayesian Network," Energies, MDPI, vol. 13(19), pages 1-27, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5108-:d:422453
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    References listed on IDEAS

    as
    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
    2. Wu, Xianguo & Liu, Huitao & Zhang, Limao & Skibniewski, Miroslaw J. & Deng, Qianli & Teng, Jiaying, 2015. "A dynamic Bayesian network based approach to safety decision support in tunnel construction," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 157-168.
    3. Mustapa, M.A. & Yaakob, O.B. & Ahmed, Yasser M. & Rheem, Chang-Kyu & Koh, K.K. & Adnan, Faizul Amri, 2017. "Wave energy device and breakwater integration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 43-58.
    4. Lei Jiang & Yiliu Liu & Xiaomin Wang & Mary Ann Lundteigen, 2019. "Operation-oriented reliability and availability evaluation for onboard high-speed train control system with dynamic Bayesian network," Journal of Risk and Reliability, , vol. 233(3), pages 455-469, June.
    5. Kwang Pil Chang & Daejun Chang & Enrico Zio, 2010. "Application of Monte Carlo Simulation for the Estimation of Production Availability in Offshore Installations," Springer Series in Reliability Engineering, in: Javier Faulin & Angel A. Juan & Sebastián Martorell & José-Emmanuel Ramírez-Márquez (ed.), Simulation Methods for Reliability and Availability of Complex Systems, chapter 0, pages 233-252, Springer.
    6. Amin, Md. Tanjin & Khan, Faisal & Imtiaz, Syed, 2018. "Dynamic availability assessment of safety critical systems using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 108-117.
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    Cited by:

    1. Dimitri V. Val, 2023. "Reliability of Marine Energy Converters," Energies, MDPI, vol. 16(8), pages 1-4, April.
    2. Yi Yang & Jannie Sønderkær Nielsen, 2021. "Availability-Based Selection of Electricity Delivery Network in Marine Conversion Systems Using Bayesian Network," Energies, MDPI, vol. 14(12), pages 1-14, June.

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