Simulator-based human reliability analysis using Bayesian network: a case study on situation awareness in engine resources management

Situational awareness (SA) is regarded as one of the important non-technical skills in constructing the seafarers’ ability in daily decision-making and performing tasks, especially in Engine Resources Management (ERM). In maritime accidents that are mainly human error, insufficient SA is the specific factor that contributes to most of incidents. To quantitatively assess SA reliability, a Bayesian network of seafarers’ performance in attaining SA in engine supervisory control is constructed. The adaptation of simulator data helped as combination along using subject matter expert input, which is a common practice in constructing human reliability analysis. Additionally, the simulator data can serve as the updating function when new data is observed. The result shows that the model can provide promising results as compared with expert expectation. Such kind of model can support the evaluation of the engine operation onboard, and mitigation can be provided to reduce the probability of human error.

---

1. Pełne imiona podano na stronie internetowej czasopisma w "Authors in other databases."

---

2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).