UAS Risk Analysis using Bayesian Belief Networks: An Application to the VirginiaTech ESPAARO

Small Unmanned Aerial Vehicles (SUAVs) are rapidly being adopted in the National Airspace (NAS) but experience a much higher failure rate than traditional aircraft. These SUAVs are quickly becoming complex enough to investigate alternative methods of failure analysis. This thesis proposes a method of expanding on the Fault Tree Analysis (FTA) method to a Bayesian Belief Network (BBN) model. FTA is demonstrated to be a special case of BBN and BBN can allow for more complex interactions between nodes than is allowed by FTA. A model can be investigated to determine the components to which failure is most sensitive and allow for redundancies or mitigations against those failures. The introduced method is then applied to the Virginia Tech ESPAARO SUAV.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Aerospace Engineering

Department dc:contributor.department

Aerospace and Ocean Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2016

Author dc:creator

• Kevorkian, Christopher George

Chairs dc:contributor.committeechair

• Woolsey, Craig A.
• Luxhoj, James T.

Committee member dc:contributor.committeemember

• Raj, Pradeep

• UAS
• Reliability
• Bayesian
• Fault Tree Analysis

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:8776

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/73047