IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6598-d655417.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6598/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/20/6598/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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).
    4. 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).
    5. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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).
    2. Ariannik, Mohamadreza & Razi-Kazemi, Ali A. & Lehtonen, Matti, 2020. "An approach on lifetime estimation of distribution transformers based on degree of polymerization," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. BahooToroody, Ahmad & Abaei, Mohammad Mahdi & Banda, Osiris Valdez & Kujala, Pentti & De Carlo, Filippo & Abbassi, Rouzbeh, 2022. "Prognostic health management of repairable ship systems through different autonomy degree; From current condition to fully autonomous ship," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Khastgir, Siddartha & Brewerton, Simon & Thomas, John & Jennings, Paul, 2021. "Systems Approach to Creating Test Scenarios for Automated Driving Systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. He, Rui & Zhu, Jingyu & Chen, Guoming & Tian, Zhigang, 2022. "A real-time probabilistic risk assessment method for the petrochemical industry based on data monitoring," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    6. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    7. Wu, Shaomin & Do, Phuc, 2017. "Editorial," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 1-3.
    8. Ferretto, D. & Mazzini, G. & Ambrosini, W. & Aldorf, R. & Hrehor, M., 2021. "Risk monitor implementation for the LVR-15 research reactor," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    9. Zhang, Xi & Liu, Dong & Tu, Haicheng & Tse, Chi Kong, 2022. "An integrated modeling framework for cascading failure study and robustness assessment of cyber-coupled power grids," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    10. Michael Felix Pacevicius & Marilia Ramos & Davide Roverso & Christian Thun Eriksen & Nicola Paltrinieri, 2022. "Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures," Energies, MDPI, vol. 15(9), pages 1-40, April.
    11. Chelouati, Mohammed & Boussif, Abderraouf & Beugin, Julie & El Koursi, El-Miloudi, 2023. "Graphical safety assurance case using Goal Structuring Notation (GSN) — challenges, opportunities and a framework for autonomous trains," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    12. Zaitseva, Elena & Levashenko, Vitaly & Rabcan, Jan, 2023. "A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Meissner, Robert & Rahn, Antonia & Wicke, Kai, 2021. "Developing prescriptive maintenance strategies in the aviation industry based on a discrete-event simulation framework for post-prognostics decision making," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    14. Ramin Moradi & Andrés Ruiz-Tagle Palazuelos & Enrique Lopez Droguett & Katrina M Groth, 2023. "Toward a framework for risk monitoring of complex engineering systems with online operational data: A deep learning-based solution," Journal of Risk and Reliability, , vol. 237(5), pages 910-921, October.
    15. Bolbot, Victor & Kulkarni, Ketki & Brunou, Päivi & Banda, Osiris Valdez & Musharraf, Mashrura, 2022. "Developments and research directions in maritime cybersecurity: A systematic literature review and bibliometric analysis," International Journal of Critical Infrastructure Protection, Elsevier, vol. 39(C).
    16. Bolbot, Victor & Trivyza, Nikoletta L. & Theotokatos, Gerasimos & Boulougouris, Evangelos & Rentizelas, Athanasios & Vassalos, Dracos, 2020. "Cruise ships power plant optimisation and comparative analysis," Energy, Elsevier, vol. 196(C).
    17. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    18. Wu, Gongyu & Li, Meiyan & Li, Zhaojun Steven, 2021. "A Gene Importance based Evolutionary Algorithm (GIEA) for identifying critical nodes in Cyber–Physical Power Systems," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    19. Chatterjee, Samrat & Thekdi, Shital, 2020. "An iterative learning and inference approach to managing dynamic cyber vulnerabilities of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    20. Feng, Jian Rui & Yu, Guanghui & Zhao, Mengke & Zhang, Jiaqing & Lu, Shouxiang, 2022. "Dynamic risk assessment framework for industrial systems based on accidents chain theory: The case study of fire and explosion risk of UHV converter transformer," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6598-:d:655417. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.