aerialvehicles {bnRep}R Documentation

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.


[Package bnRep version 0.0.1 Index]