aerialvehicles: aerialvehicles Bayesian Network

In bnRep: A Repository of Bayesian Networks from the Academic Literature

aerialvehicles Bayesian Network

Description

Analysis and assessment of risks to public safety from unmanned aerial vehicles using fault tree analysis and Bayesian network.

Format

A discrete Bayesian network to analyze critical risks associated with unmanned aerial vehicles. Probabilities were given within the referenced paper. The vertices are:

X1

Mechanical failures (yes, no);

X2

Battery failures (yes, no);

X3

Flight control system failures (yes, no);

X4

Gust (yes, no);

X5

Rain and snow (yes, no);

X6

Thunderstorm (yes, no);

X7

Visibility (yes, no);

X8

Communication link failures (yes, no);

X9

GPS failures (yes, no);

X10

Ostacles (yes, no);

X11

Route planning issues (yes, no);

X12

Unclear airspace division (yes, no);

X13

Unqualified knowledge and skills (yes, no);

X14

Weak safety awareness (yes, no);

X15

Lack of experience (yes, no);

X16

Careless (yes, no);

X17

Fatigue (yes, no);

X18

Violations (yes, no);

X19

Lack of legal awareness (yes, no);

X20

Psychological problems (yes, no);

X21

Undefined subject of supervision responsibility (yes, no);

X22

Lack of unified industry standard (yes, no);

X23

Unclear airworthiness certification procedures (yes, no);

X24

Long flight approval cycle (yes, no);

X25

Weak laws and regulations (yes, no);

X26

Inadequate training system (yes, no);

X27

Lack of supervision system (yes, no);

Value

An object of class bn.fit. Refer to the documentation of bnlearn for details.

References

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.

bnRep documentation built on April 12, 2025, 1:13 a.m.