A risk evaluation method for human-machine interaction in emergencies based on multiple mental models-driven situation assessment.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to evaluate risk in human-machine interaction in emergencies. The probabilities were given within the referenced paper. The vertices are:
Trim state (normal, abnormal);
Flap position (retracted, extended);
Cabin pressurization mode setting (automatic, manual);
Equipment cooling fan state (normal, failure);
Takeoff configuration (correct, wrong);
Cabine pressure (normal, low);
Equipment cooling airflow (normal, low);
Oxygen mask deployment (yes, no);
Trim state indication (normal, abnormal);
Flap position indication (retracted, extended);
Cabin pressurization mode setting indication (automatic, manual);
Equipment cooling fan circuit break indication (on, off);
Cabine altitued warning (yes, no);
Cabin low pressure light (illuminated, extinguished);
Oxygen mask deployment light (illuminated, extinguished);
Equipment cooling OFF light (illuminated, extinguished);
Equipment cooling fan OFF light (illuminated, extinguished);
Guo, J., Ma, S., Zeng, S., Che, H., & Pan, X. (2024). A risk evaluation method for human-machine interaction in emergencies based on multiple mental models-driven situation assessment. Reliability Engineering & System Safety, 110444.