The University of Southampton
University of Southampton Institutional Repository

A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions

A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions
A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions
Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability and environmental factors, and cannot be determined through analytical means alone. An alternative approach – formal expert judgment – is a time-consuming process; consequently a method is needed to broaden the applicability of judgments beyond the narrow confines of an elicitation for a defined environment. We propose and explore a solution founded on a Bayesian Belief Network (BBN), where the results of the expert judgment elicitation are taken as the initial prior probability of loss due to failure. The network topology captures the causal effects of the environment separately on the vehicle and on the support platform, and combines these to produce an updated probability of loss due to failure. An extended version of the Kaplan–Meier estimator is then used to update the mission risk profile with travelled distance. Sensitivity analysis of the BBN is presented and a case study of Autosub3 AUV deployment in the Amundsen Sea is discussed in detail.
Bayesian networks, Survival statistics, Expert judgment elicitation, Autonomous vehicles
55-67
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Griffiths, Gwyn
a0447dd5-c7cd-4bc9-b945-0da7ab236a08
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Griffiths, Gwyn
a0447dd5-c7cd-4bc9-b945-0da7ab236a08

Brito, Mario and Griffiths, Gwyn (2016) A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions. Reliability Engineering & System Safety, 146, 55-67. (doi:10.1016/j.ress.2015.10.004).

Record type: Article

Abstract

Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability and environmental factors, and cannot be determined through analytical means alone. An alternative approach – formal expert judgment – is a time-consuming process; consequently a method is needed to broaden the applicability of judgments beyond the narrow confines of an elicitation for a defined environment. We propose and explore a solution founded on a Bayesian Belief Network (BBN), where the results of the expert judgment elicitation are taken as the initial prior probability of loss due to failure. The network topology captures the causal effects of the environment separately on the vehicle and on the support platform, and combines these to produce an updated probability of loss due to failure. An extended version of the Kaplan–Meier estimator is then used to update the mission risk profile with travelled distance. Sensitivity analysis of the BBN is presented and a case study of Autosub3 AUV deployment in the Amundsen Sea is discussed in detail.

Text
A BayesianApproach to Predicting Risk of AUV Loss During their Missions.pdf - Accepted Manuscript
Download (1MB)

More information

e-pub ahead of print date: 20 October 2015
Published date: February 2016
Keywords: Bayesian networks, Survival statistics, Expert judgment elicitation, Autonomous vehicles
Organisations: National Oceanography Centre, Southampton Business School

Identifiers

Local EPrints ID: 69181
URI: http://eprints.soton.ac.uk/id/eprint/69181
PURE UUID: 96bbd9d2-1d42-4c3c-9b03-5652a210ec5f

Catalogue record

Date deposited: 22 Oct 2009
Last modified: 01 Jul 2020 16:30

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×