Analysis and assessment of risks to public safety from unmanned aerial vehicles using fault tree analysis and Bayesian network.
An object of class bn.fit. Refer to the documentation of bnlearn for details.
A discrete Bayesian network to analyze critical risks associated with unmanned aerial vehicles. Probabilities were given within the referenced paper. The vertices are:
Mechanical failures (yes, no);
Battery failures (yes, no);
Flight control system failures (yes, no);
Gust (yes, no);
Rain and snow (yes, no);
Thunderstorm (yes, no);
Visibility (yes, no);
Communication link failures (yes, no);
GPS failures (yes, no);
Ostacles (yes, no);
Route planning issues (yes, no);
Unclear airspace division (yes, no);
Unqualified knowledge and skills (yes, no);
Weak safety awareness (yes, no);
Lack of experience (yes, no);
Careless (yes, no);
Fatigue (yes, no);
Violations (yes, no);
Lack of legal awareness (yes, no);
Psychological problems (yes, no);
Undefined subject of supervision responsibility (yes, no);
Lack of unified industry standard (yes, no);
Unclear airworthiness certification procedures (yes, no);
Long flight approval cycle (yes, no);
Weak laws and regulations (yes, no);
Inadequate training system (yes, no);
Lack of supervision system (yes, no);
Xiao, Q., Li, Y., Luo, F., & Liu, H. (2023). Analysis and assessment of risks to public safety from unmanned aerial vehicles using fault tree analysis and Bayesian network. Technology in Society, 73, 102229.