Risk analysis methodology using STPA-based Bayesian network- applied to remote pilotage operation

Abstract

The maritime industry is currently ongoing into a digital transformation to develop cleaner, safer and smarter transport services. Establishing such services requires identifying and assessing new emerging risks such as software and design flaws. Thus, suitable hazard identification and risk analysis methods must be developed and implemented for these complex services. This study aims to develop a novel risk analysis methodology by integrating Systems Theoretic Process Analysis, Bayesian Network, Noisy-OR gates, Parent divorcing technique and Sub-modelling. The effectiveness of the proposed methodology is demonstrated through a case study of Remote pilotage operation. The results show that the methodology can be applied to complex operations to assess the propagation of risks from a single fault or failure in a system to the hazards, accidents and incidents, and ultimately the losses. Furthermore, it is demonstrated how the remote pilotage risk model can support pilots and pilotage companies in real-time decision-making by estimating the likelihood of losses in case of a single fault or failure.