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A Bayesian approach to risk modeling of autonomous subsea intervention operations

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  • Hegde, Jeevith
  • Utne, Ingrid Bouwer
  • Schjølberg, Ingrid
  • Thorkildsen, Brede

Abstract

The introduction of autonomy in subsea operations may affect operational risk related to Inspection, Maintenance, and Repair (IMR). This article proposes a Bayesian Belief Network (BBN) to model the risk affecting autonomous subsea IMR operations. The proposed BBN risk model can be used to calculate the probability of aborting an autonomous subsea IMR operation. The nodes of the BBN are structured using three main categories, namely technical, organizational, and operational. The BBN is tested for five unique scenarios using a scenario generation methodology for the operational phase of the autonomous IMR operation. The BBN is quantified by conducting a workshop involving industry experts. The results from the proposed model may provide a useful aid to human supervisors in their decision-making processes. The model is verified for five scenarios, but it is capable of incorporating and calculating risk for other combinations of scenarios.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:175:y:2018:i:c:p:142-159
    DOI: 10.1016/j.ress.2018.03.019
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    Cited by:

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    4. Sutrisno Sutrisno & Sunarsih Sunarsih & Widowati Widowati, 2022. "A Piecewise Objective Probabilistic Optimization Approach as Decision Making for Supplier Selection and Inventory Management With Price Discount," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 15(1), pages 1-17, January.
    5. 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).
    6. Colombo, Danilo & Lima, Gilson Brito Alves & Pereira, Danillo Roberto & Papa, João P., 2020. "Regression-based finite element machines for reliability modeling of downhole safety valves," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    7. 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).
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    9. Xu, Sheng & Kim, Ekaterina & Haugen, Stein & Zhang, Mingyang, 2022. "A Bayesian network risk model for predicting ship besetting in ice during convoy operations along the Northern Sea Route," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

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