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A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions

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  • Brito, Mario
  • Griffiths, Gwyn

Abstract

Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability and environmental factors, and cannot be determined through analytical means alone. An alternative approach – formal expert judgment – is a time-consuming process; consequently a method is needed to broaden the applicability of judgments beyond the narrow confines of an elicitation for a defined environment. We propose and explore a solution founded on a Bayesian Belief Network (BBN), where the results of the expert judgment elicitation are taken as the initial prior probability of loss due to failure. The network topology captures the causal effects of the environment separately on the vehicle and on the support platform, and combines these to produce an updated probability of loss due to failure. An extended version of the Kaplan–Meier estimator is then used to update the mission risk profile with travelled distance. Sensitivity analysis of the BBN is presented and a case study of Autosub3 AUV deployment in the Amundsen Sea is discussed in detail.

Suggested Citation

  • Brito, Mario & Griffiths, Gwyn, 2016. "A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 55-67.
    • Handle: RePEc:eee:reensy:v:146:y:2016:i:c:p:55-67
    DOI: 10.1016/j.ress.2015.10.004
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    References listed on IDEAS

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    1. Matellini, D.B. & Wall, A.D. & Jenkinson, I.D. & Wang, J. & Pritchard, R., 2013. "Modelling dwelling fire development and occupancy escape using Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 75-91.
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    Cited by:

    1. Tzu Yang Loh & Mario P. Brito & Neil Bose & Jingjing Xu & Kiril Tenekedjiev, 2020. "Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic," Risk Analysis, John Wiley & Sons, vol. 40(4), pages 818-841, April.
    2. Mario P. Brito & Ian G. J. Dawson, 2020. "Predicting the Validity of Expert Judgments in Assessing the Impact of Risk Mitigation Through Failure Prevention and Correction," Risk Analysis, John Wiley & Sons, vol. 40(10), pages 1928-1943, October.
    3. Utne, Ingrid Bouwer & Rokseth, Børge & Sørensen, Asgeir J. & Vinnem, Jan Erik, 2020. "Towards supervisory risk control of autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 196(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. Chen, Xi & Bose, Neil & Brito, Mario & Khan, Faisal & Thanyamanta, Bo & Zou, Ting, 2021. "A Review of Risk Analysis Research for the Operations of Autonomous Underwater Vehicles," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Thieme, Christoph A. & Utne, Ingrid B., 2017. "Safety performance monitoring of autonomous marine systems," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 264-275.
    7. Johansen, Thomas & Blindheim, Simon & Torben, Tobias Rye & Utne, Ingrid Bouwer & Johansen, Tor Arne & Sørensen, Asgeir J., 2023. "Development and testing of a risk-based control system for autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Tzu Yang Loh & Mario P. Brito & Neil Bose & Jingjing Xu & Kiril Tenekedjiev, 2020. "Human Error in Autonomous Underwater Vehicle Deployment: A System Dynamics Approach," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1258-1278, June.
    9. Christoph Alexander Thieme & Ingrid Bouwer Utne, 2017. "A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration," Journal of Risk and Reliability, , vol. 231(4), pages 446-464, August.
    10. Tzu Yang Loh & Mario P. Brito & Neil Bose & Jingjing Xu & Kiril Tenekedjiev, 2019. "A Fuzzy‐Based Risk Assessment Framework for Autonomous Underwater Vehicle Under‐Ice Missions," Risk Analysis, John Wiley & Sons, vol. 39(12), pages 2744-2765, December.
    11. Hegde, Jeevith & Utne, Ingrid Bouwer & Schjølberg, Ingrid & Thorkildsen, Brede, 2018. "A Bayesian approach to risk modeling of autonomous subsea intervention operations," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 142-159.

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