A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions

Brito, Mario; Griffiths, Gwyn. 2016 A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions. Reliability Engineering & System Safety, 146. 55-67. 10.1016/j.ress.2015.10.004

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© 2016 Elsevier B.V. This is the author’s version of a work that was accepted for publication in Reliability Engineering & System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was/will be published in Reliability Engineering & System Safety (doi:10.1016/j.ress.2015.10.004).
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Official URL: http://dx.doi.org/10.1016/j.ress.2015.10.004

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.ress.2015.10.004
ISSN:	09518320
Additional Keywords:	Bayesian networks; Survival statistics; Expert judgment elicitation; Autonomous vehicles
Date made live:	08 Jan 2016 11:02 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/512604

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.ress.2015.10.004

ISSN:

09518320

Additional Keywords:

Bayesian networks; Survival statistics; Expert judgment elicitation; Autonomous vehicles

Date made live:

08 Jan 2016 11:02 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/512604