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Dynamic Blackout Probability Monitoring System for Cruise Ship Power Plants

Author

Listed:
  • Victor Bolbot

    (Maritime Safety Research Centre, Department of Naval Architecture, Ocean & Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
    Marine Technology, Department of Mechanical Engineering, School of Engineering, Aalto University, 00340 Espoo, Finland)

  • Gerasimos Theotokatos

    (Maritime Safety Research Centre, Department of Naval Architecture, Ocean & Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK)

  • Rainer Hamann

    (DNV, Regulatory Affairs, 20457 Hamburg, Germany)

  • George Psarros

    (DNV, Group Research & Development, Maritime Transport, 1363 Høvik, Norway)

  • Evangelos Boulougouris

    (Maritime Safety Research Centre, Department of Naval Architecture, Ocean & Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK)

Abstract

Stringent environmental regulations and efforts to improve the shipping operations sustainability have resulted in designing and employing more complex configurations for the ship power plants systems and the implementation of digitalised functionalities. Due to these systems complexity, critical situations arising from the components and subsystem failures, which may lead to accidents, require timely detection and mitigation. This study aims at enhancing the safety of ship complex systems and their operation by developing the concept of an integrated monitoring safety system that employs existing safety models and data fusion from shipboard sensors. Detailed Fault Trees that model the blackout top event, representing the sailing modes of a cruise ship and the operating modes of its plant, are employed. Shipboard sensors’ measurements acquired by the cruise ship alarm and monitoring system are integrated with these Fault Trees to account for the acquired shipboard information on the investigated power plant configuration and its components operating conditions, thus, facilitating the estimation of the blackout probability time variation as well as the dynamic criticality assessment of the power plant components. The proposed concept is verified by using a virtual simulation environment developed in Matlab/Simulink. This study supports the dynamic assessment of the ship power plants and therefore benefits the decision-making for enhancing the plant safety during operations.

Suggested Citation

  • Victor Bolbot & Gerasimos Theotokatos & Rainer Hamann & George Psarros & Evangelos Boulougouris, 2021. "Dynamic Blackout Probability Monitoring System for Cruise Ship Power Plants," Energies, MDPI, vol. 14(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6598-:d:655417
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    References listed on IDEAS

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    1. Bolbot, Victor & Theotokatos, Gerasimos & Bujorianu, Luminita Manuela & Boulougouris, Evangelos & Vassalos, Dracos, 2019. "Vulnerabilities and safety assurance methods in Cyber-Physical Systems: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 179-193.
    2. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
    3. Xing, Jinduo & Zeng, Zhiguo & Zio, Enrico, 2019. "A framework for dynamic risk assessment with condition monitoring data and inspection data," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    4. Abaei, Mohammad Mahdi & Hekkenberg, Robert & BahooToroody, Ahmad, 2021. "A multinomial process tree for reliability assessment of machinery in autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    5. Eriksen, Stig & Utne, Ingrid Bouwer & Lützen, Marie, 2021. "An RCM approach for assessing reliability challenges and maintenance needs of unmanned cargo ships," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
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    Cited by:

    1. Tsoumpris, Charalampos & Theotokatos, Gerasimos, 2023. "A decision-making approach for the health-aware energy management of ship hybrid power plants," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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