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A multinomial process tree for reliability assessment of machinery in autonomous ships

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  • Abaei, Mohammad Mahdi
  • Hekkenberg, Robert
  • BahooToroody, Ahmad

Abstract

Maritime Autonomous Surface Ships have received a significant amount of attention in recent projects. They promise a reduction in marine accidents and mitigation of human errors. Most of the ongoing research effort is directed toward autonomous navigation and cybersecurity. However, the importance of a machinery plant in the engine room that can operate reliably without human attendance is hardly investigated. To prevent failures in such systems and extend the interval between required human interventions, it is essential to improve their reliability. This paper aims to present a systematic approach to evaluate the reliability of an autonomous system under the influence of uncertain disruptions and to predict failure rates of unattended machinery plants. A Multinomial Process Tree is used to model failures in the main failure-sensitive components. Hierarchical Bayesian Inference is adopted to facilitate the prediction of frequencies of disruptive events and estimate the entire system's failure rate. The outcome of this research enables design strategies to improve the reliability of autonomous ships and prevent Fatal Technical Failure during the operation. This allows assessing whether a given machinery plant is sufficiently reliable to be used on unmanned ships. A case study is considered to demonstrate the application of the presented method.

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  • 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).
    • Handle: RePEc:eee:reensy:v:210:y:2021:i:c:s0951832021000508
    DOI: 10.1016/j.ress.2021.107484
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    1. 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.
    2. Cheng, Tingting & Utne, Ingrid Bouwer & Wu, Bing & Wu, Qing, 2023. "A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Abaei, Mohammad Mahdi & Hekkenberg, Robert & BahooToroody, Ahmad & Banda, Osiris Valdez & van Gelder, Pieter, 2022. "A probabilistic model to evaluate the resilience of unattended machinery plants in autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. 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).
    5. 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).
    6. Zhang, Wenjun & Zhang, Yingjun & Zhang, Chuang, 2024. "Research on risk assessment of maritime autonomous surface ships based on catastrophe theory," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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