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 reli...
Main Authors: | , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2016
|
Subjects: | |
Online Access: | https://eprints.soton.ac.uk/69181/ https://eprints.soton.ac.uk/69181/1/A%2520BayesianApproach%2520to%2520Predicting%2520Risk%2520of%2520AUV%2520Loss%2520During%2520their%2520Missions.pdf |
id |
ftsouthampton:oai:eprints.soton.ac.uk:69181 |
---|---|
record_format |
openpolar |
spelling |
ftsouthampton:oai:eprints.soton.ac.uk:69181 2023-07-30T03:56:05+02:00 A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions Brito, Mario Griffiths, Gwyn 2016-02 text https://eprints.soton.ac.uk/69181/ https://eprints.soton.ac.uk/69181/1/A%2520BayesianApproach%2520to%2520Predicting%2520Risk%2520of%2520AUV%2520Loss%2520During%2520their%2520Missions.pdf en eng https://eprints.soton.ac.uk/69181/1/A%2520BayesianApproach%2520to%2520Predicting%2520Risk%2520of%2520AUV%2520Loss%2520During%2520their%2520Missions.pdf 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 <http://dx.doi.org/10.1016/j.ress.2015.10.004>). Article PeerReviewed 2016 ftsouthampton 2023-07-09T21:06:35Z 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. Article in Journal/Newspaper Amundsen Sea University of Southampton: e-Prints Soton Amundsen Sea Meier ENVELOPE(-45.900,-45.900,-60.633,-60.633) |
institution |
Open Polar |
collection |
University of Southampton: e-Prints Soton |
op_collection_id |
ftsouthampton |
language |
English |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Brito, Mario Griffiths, Gwyn |
spellingShingle |
Brito, Mario Griffiths, Gwyn A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions |
author_facet |
Brito, Mario Griffiths, Gwyn |
author_sort |
Brito, Mario |
title |
A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions |
title_short |
A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions |
title_full |
A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions |
title_fullStr |
A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions |
title_full_unstemmed |
A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions |
title_sort |
bayesian approach to predicting risk of loss during autonomous underwater vehicle missions |
publishDate |
2016 |
url |
https://eprints.soton.ac.uk/69181/ https://eprints.soton.ac.uk/69181/1/A%2520BayesianApproach%2520to%2520Predicting%2520Risk%2520of%2520AUV%2520Loss%2520During%2520their%2520Missions.pdf |
long_lat |
ENVELOPE(-45.900,-45.900,-60.633,-60.633) |
geographic |
Amundsen Sea Meier |
geographic_facet |
Amundsen Sea Meier |
genre |
Amundsen Sea |
genre_facet |
Amundsen Sea |
op_relation |
https://eprints.soton.ac.uk/69181/1/A%2520BayesianApproach%2520to%2520Predicting%2520Risk%2520of%2520AUV%2520Loss%2520During%2520their%2520Missions.pdf 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 <http://dx.doi.org/10.1016/j.ress.2015.10.004>). |
_version_ |
1772810831365406720 |