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Safety performance monitoring of autonomous marine systems

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  • Thieme, Christoph A.
  • Utne, Ingrid B.

Abstract

The marine environment is vast, harsh, and challenging. Unanticipated faults and events might lead to loss of vessels, transported goods, collected scientific data, and business reputation. Hence, systems have to be in place that monitor the safety performance of operation and indicate if it drifts into an intolerable safety level. This article proposes a process for developing safety indicators for the operation of autonomous marine systems (AMS). The condition of safety barriers and resilience engineering form the basis for the development of safety indicators, synthesizing and further adjusting the dual assurance and the resilience based early warning indicator (REWI) approaches. The article locates the process for developing safety indicators in the system life cycle emphasizing a timely implementation of the safety indicators. The resulting safety indicators reflect safety in AMS operation and can assist in planning of operations, in daily operational decision-making, and identification of improvements. Operation of an autonomous underwater vehicle (AUV) exemplifies the process for developing safety indicators and their implementation. The case study shows that the proposed process leads to a comprehensive set of safety indicators. It is expected that application of the resulting safety indicators consequently will contribute to safer operation of current and future AMS.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:159:y:2017:i:c:p:264-275
    DOI: 10.1016/j.ress.2016.11.024
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    References listed on IDEAS

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    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    2. Leveson, Nancy, 2015. "A systems approach to risk management through leading safety indicators," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 17-34.
    3. 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.
    4. Paltrinieri, Nicola & Øien, Knut & Cozzani, Valerio, 2012. "Assessment and comparison of two early warning indicator methods in the perspective of prevention of atypical accident scenarios," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 21-31.
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    Cited by:

    1. Wróbel, Krzysztof & Montewka, Jakub & Kujala, Pentti, 2018. "Towards the development of a system-theoretic model for safety assessment of autonomous merchant vessels," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 209-224.
    2. 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).
    3. Wróbel, Krzysztof & Montewka, Jakub & Kujala, Pentti, 2017. "Towards the assessment of potential impact of unmanned vessels on maritime transportation safety," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 155-169.
    4. 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.

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