PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Susanna Kristensen Co-author(s): Yiliu Liu yiliu.liu@ntnu.no Ingrid Bouwer Utne ingrid.b.utne@ntnu.no

Dynamic risk analysis of maritime autonomous surface ships
Background In the future, ships may utilize different technologically advanced solutions to perform their missions, for example supervisory risk control and intelligent power management (Utne et al., 2020), use of alternative energy sources (Pan et al., 2021), and use of sensors and cameras for ocean surveillance and navigation (Pizarro & Singh, 2003), and more. Maritime autonomous surface ships (MASS) are under development (IMO, 2021). The risks during MASS operations will be affected by dynamic factors relating to the operation environment, the technical systems, and the mission specifications. This introduces the need for dynamic risk analysis. For MASS, sensors, actuators, and computers, gradually take over the task previously performed by the crew (Utne et al., 2017). The implementation of autonomous functionalities on ships can have many advantages. However, it also makes the realization of necessary functions on board the vessel, such as maintaining an adequate level of situational awareness and performing safe navigation, more dependent on technical components. Better situational awareness may be achieved with more sensors and higher sampling frequency. However, this would require more power. Green energy sources are a part of the future of maritime system operations. Ships can use for example batteries, fuel cells, solar power, or wind energy, together with or instead of the conventional combustion engines (Pan et al., 2021). A challenge related to the use of green energy sources is the dependence on environmental factors, such as sun or wind, for generating power. These factors are outside the control of the system operator, which may add to the challenge of providing a stable energy supply for propulsion and on-board systems, compared to when combustion engines are used. The importance of a stable power supply to power the on-board systems can have implications for safety, and hence also for decision-making during operation. Objective The objective of this paper is to develop a method for performing dynamic risk analysis for MASS, where the objective is to investigate the impact of inadequate situational awareness and loss of power on the mission performance. For MASS, a trade-off between maintaining a sufficient level of situational awareness and avoiding a complete loss of power, must be made. By including risk in this decision, safer and more efficient operations can be achieved. Method A method for modeling the risks related to MASS operations is proposed. Dynamic Bayesian network is used to model the risk. Dynamic Bayesian networks facilitates the inclusion of time-dependent factors and their effect on risk (Jensen & Nielsen, 2007). This makes it a suitable method for modelling risk related to MASS, as such systems are affected by dynamic factors. The method is applied to a case study. The risk of not performing the mission for the AutoNaut unmanned surface vessel (USV) is analysed. The USV is meant for performing scientific missions in the ocean and uses wind and solar power for propulsion and for powering all on-board systems (Dallolio et al., 2019). (Expected) results The results from this study includes a proposed methodology for analysis risk for MASS operations. The proposed methodology can be used to identify relevant factors to include in the risk analysis of a general MASS, with a focus on performing the intended missions while maintaining an adequate level of situational awareness and avoiding loss of power. The resulting risk model may be used for decision support for operators during the planning and performance of MASS operations. By using dynamic Bayesian networks, the development of risk during a mission can be modelled, and critical time steps can be identified. This will give the operators an indication for when more resources must be allocated for reducing risks. The results from the case study show that the risk related to the USV operation changes with time and is affected by environmental factors. It also shows that the prioritization between using power on maintaining a high level of situational awareness and preserving power when there is limited opportunity to generate power is important for safe and efficient operation of the USV. References Utne, I. B., Rokseth, B., Sørensen, A. J., Vinnem, J. E., 2020. Towards supervisory risk control of autonomous ships. Reliability engineering & system safety, 196, pp. 106757. Pizarro, O., Singh, H., 2003. Toward larg-area mosaicing for underwater scientific applications. IEEE journal of oceanic engineering, 28, pp. 651-672. International Maritime Organisation, 2021. In focus: Autonomous shipping. https://www.imo.org/en/MediaCentre/HotTopics/Pages/Autonomous-shipping.aspx. Pan, P., Sun, Y., Yuan, C., Yan, X., Tang, X., 2021. Research progress on ship power systems integrated with new energy sources: A review. Renewable and sustainable energy reviews 144, 111048. Dallolio, A., Agdal, B., Zolich, A., Alfredsen, J.A., Johansen, T.A., 2019. Long-endurance green energy autonomous surface vehicle control architecture. OCEANS 2019 MTS/IEEE SEATTLE , 1–10. Utne, I.U., Sørensen, A.J., Schjølberg, I., 2017. Risk management of autonomous marine systems and operations, in: Proceedings of the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, pp. 1–10. Jensen, F.V., Nielsen, T.D., 2007. Bayesian Networks and Decision Graphs. Springer, New York, NY.

Dynamic risk analysis of maritime autonomous surface ships

Background In the future, ships may utilize different technologically advanced solutions to perform their missions, for example supervisory risk control and intelligent power management (Utne et al., 2020), use of alternative energy sources (Pan et al., 2021), and use of sensors and cameras for ocean surveillance and navigation (Pizarro & Singh, 2003), and more. Maritime autonomous surface ships (MASS) are under development (IMO, 2021). The risks during MASS operations will be affected by dynamic factors relating to the operation environment, the technical systems, and the mission specifications. This introduces the need for dynamic risk analysis. For MASS, sensors, actuators, and computers, gradually take over the task previously performed by the crew (Utne et al., 2017). The implementation of autonomous functionalities on ships can have many advantages. However, it also makes the realization of necessary functions on board the vessel, such as maintaining an adequate level of situational awareness and performing safe navigation, more dependent on technical components. Better situational awareness may be achieved with more sensors and higher sampling frequency. However, this would require more power. Green energy sources are a part of the future of maritime system operations. Ships can use for example batteries, fuel cells, solar power, or wind energy, together with or instead of the conventional combustion engines (Pan et al., 2021). A challenge related to the use of green energy sources is the dependence on environmental factors, such as sun or wind, for generating power. These factors are outside the control of the system operator, which may add to the challenge of providing a stable energy supply for propulsion and on-board systems, compared to when combustion engines are used. The importance of a stable power supply to power the on-board systems can have implications for safety, and hence also for decision-making during operation. Objective The objective of this paper is to develop a method for performing dynamic risk analysis for MASS, where the objective is to investigate the impact of inadequate situational awareness and loss of power on the mission performance. For MASS, a trade-off between maintaining a sufficient level of situational awareness and avoiding a complete loss of power, must be made. By including risk in this decision, safer and more efficient operations can be achieved. Method A method for modeling the risks related to MASS operations is proposed. Dynamic Bayesian network is used to model the risk. Dynamic Bayesian networks facilitates the inclusion of time-dependent factors and their effect on risk (Jensen & Nielsen, 2007). This makes it a suitable method for modelling risk related to MASS, as such systems are affected by dynamic factors. The method is applied to a case study. The risk of not performing the mission for the AutoNaut unmanned surface vessel (USV) is analysed. The USV is meant for performing scientific missions in the ocean and uses wind and solar power for propulsion and for powering all on-board systems (Dallolio et al., 2019). (Expected) results The results from this study includes a proposed methodology for analysis risk for MASS operations. The proposed methodology can be used to identify relevant factors to include in the risk analysis of a general MASS, with a focus on performing the intended missions while maintaining an adequate level of situational awareness and avoiding loss of power. The resulting risk model may be used for decision support for operators during the planning and performance of MASS operations. By using dynamic Bayesian networks, the development of risk during a mission can be modelled, and critical time steps can be identified. This will give the operators an indication for when more resources must be allocated for reducing risks. The results from the case study show that the risk related to the USV operation changes with time and is affected by environmental factors. It also shows that the prioritization between using power on maintaining a high level of situational awareness and preserving power when there is limited opportunity to generate power is important for safe and efficient operation of the USV. References Utne, I. B., Rokseth, B., Sørensen, A. J., Vinnem, J. E., 2020. Towards supervisory risk control of autonomous ships. Reliability engineering & system safety, 196, pp. 106757. Pizarro, O., Singh, H., 2003. Toward larg-area mosaicing for underwater scientific applications. IEEE journal of oceanic engineering, 28, pp. 651-672. International Maritime Organisation, 2021. In focus: Autonomous shipping. https://www.imo.org/en/MediaCentre/HotTopics/Pages/Autonomous-shipping.aspx. Pan, P., Sun, Y., Yuan, C., Yan, X., Tang, X., 2021. Research progress on ship power systems integrated with new energy sources: A review. Renewable and sustainable energy reviews 144, 111048. Dallolio, A., Agdal, B., Zolich, A., Alfredsen, J.A., Johansen, T.A., 2019. Long-endurance green energy autonomous surface vehicle control architecture. OCEANS 2019 MTS/IEEE SEATTLE , 1–10. Utne, I.U., Sørensen, A.J., Schjølberg, I., 2017. Risk management of autonomous marine systems and operations, in: Proceedings of the ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering, pp. 1–10. Jensen, F.V., Nielsen, T.D., 2007. Bayesian Networks and Decision Graphs. Springer, New York, NY.

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Lead Author Name: Susanna Kristensen (susanna.d.kristensen@ntnu.no)

Bio: PhD student at the Department of Marine Technology at the Norwegian University of Science and Technology (NTNU). I have a master's degree in Marine Technology from the Department of Marine Technology at (NTNU) from 2021. The topic of my PhD research is online risk modeling of autonomous ships. The research is performed as a part of the SFI Autoship research project.

Country: Norway
Company: Norwegian University of Science and Technology
Job Title: PhD student

PSAM 16 Conference Paper Overview

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