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Autonomy has allowed the maritime industry to design integrated systems leading to the concept of maritime autonomous surface ships. As research towards autonomous operations increases seafarers must be equipped with the knowledge of how to react to onboard system faults and threats to the safety of crew, vessel and cargo. Consequently, the maritime industry may utilise bridge simulators to train seafarers in autonomous operations. By integrating simulation into navigational officer training, it is possible to aid the development of seafarers fault recognition patterns. Moreover, simulation training can provide seafarers with the knowledge to be proactive in fault finding over reactive. Therefore, this study is conducted in a navigational simulator and investigates the fault recognition patterns of seafarers during realistic watch conditions with alternative tasks i.e. paperwork. Moreover, a novel Event Tree Analysis method is proposed to analyse the performance of seafarers and effectiveness of human machine relationship. The study found a low percentage of candidates successfully reacted to all faults and without additional alarms the vessel may have resulted in further danger. Applying the methodology and data assimilated from the study could aid the development of navigational officer short courses, developing seafarers behavioural skills which complement their technical talents. [ABSTRACT FROM AUTHOR]

The level of automation around the world has grown significantly over the past few decades, and continues to do so. There are many reasons behind this trend, such as safety and potential economic benefits. However, when automation fails or behaves unexpectedly, the impact on the human operator can be severe. In a safety critical operation, such as on the bridge of a ship, the consequences could be catastrophic. The research presented in this paper aims to improve understanding in this important area of study for the maritime industry. An experiment was conducted to assess the awareness of deck officer cadets in the recognition of a developing emergency situation due to failure of the autopilot. Using the results from this experiment and experiences from the aviation industry, the paper provides a potential strategy to improve automation monitoring and accuracy of situation awareness. This has led to the identification of opportunities to improve human-machine interaction. [ABSTRACT FROM AUTHOR]