Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments

Autonomous underwater vehicles (AUVs) are used increasingly to explore hazardous marine environments. Risk assessment for such complex systems is based on subjective judgment and expert knowledge as much as on hard statistics. Here, we describe the use of a risk management process tailored to AUV op...

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Published in:Risk Analysis
Main Authors: Mario Paulo Brito, Gwyn Griffiths, Peter Challenor
Format: Article in Journal/Newspaper
Language:unknown
Subjects:
Online Access:https://doi.org/10.1111/j.1539-6924.2010.01476.x
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spelling ftrepec:oai:RePEc:wly:riskan:v:30:y:2010:i:12:p:1771-1788 2023-05-15T13:53:42+02:00 Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments Mario Paulo Brito Gwyn Griffiths Peter Challenor https://doi.org/10.1111/j.1539-6924.2010.01476.x unknown https://doi.org/10.1111/j.1539-6924.2010.01476.x article ftrepec https://doi.org/10.1111/j.1539-6924.2010.01476.x 2020-12-04T13:31:06Z Autonomous underwater vehicles (AUVs) are used increasingly to explore hazardous marine environments. Risk assessment for such complex systems is based on subjective judgment and expert knowledge as much as on hard statistics. Here, we describe the use of a risk management process tailored to AUV operations, the implementation of which requires the elicitation of expert judgment. We conducted a formal judgment elicitation process where eight world experts in AUV design and operation were asked to assign a probability of AUV loss given the emergence of each fault or incident from the vehicle's life history of 63 faults and incidents. After discussing methods of aggregation and analysis, we show how the aggregated risk estimates obtained from the expert judgments were used to create a risk model. To estimate AUV survival with mission distance, we adopted a statistical survival function based on the nonparametric Kaplan‐Meier estimator. We present theoretical formulations for the estimator, its variance, and confidence limits. We also present a numerical example where the approach is applied to estimate the probability that the Autosub3 AUV would survive a set of missions under Pine Island Glacier, Antarctica in January–March 2009. Article in Journal/Newspaper Antarc* Antarctica Pine Island Pine Island Glacier RePEc (Research Papers in Economics) Pine Island Glacier ENVELOPE(-101.000,-101.000,-75.000,-75.000) Meier ENVELOPE(-45.900,-45.900,-60.633,-60.633) Risk Analysis 30 12 1771 1788
institution Open Polar
collection RePEc (Research Papers in Economics)
op_collection_id ftrepec
language unknown
description Autonomous underwater vehicles (AUVs) are used increasingly to explore hazardous marine environments. Risk assessment for such complex systems is based on subjective judgment and expert knowledge as much as on hard statistics. Here, we describe the use of a risk management process tailored to AUV operations, the implementation of which requires the elicitation of expert judgment. We conducted a formal judgment elicitation process where eight world experts in AUV design and operation were asked to assign a probability of AUV loss given the emergence of each fault or incident from the vehicle's life history of 63 faults and incidents. After discussing methods of aggregation and analysis, we show how the aggregated risk estimates obtained from the expert judgments were used to create a risk model. To estimate AUV survival with mission distance, we adopted a statistical survival function based on the nonparametric Kaplan‐Meier estimator. We present theoretical formulations for the estimator, its variance, and confidence limits. We also present a numerical example where the approach is applied to estimate the probability that the Autosub3 AUV would survive a set of missions under Pine Island Glacier, Antarctica in January–March 2009.
format Article in Journal/Newspaper
author Mario Paulo Brito
Gwyn Griffiths
Peter Challenor
spellingShingle Mario Paulo Brito
Gwyn Griffiths
Peter Challenor
Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments
author_facet Mario Paulo Brito
Gwyn Griffiths
Peter Challenor
author_sort Mario Paulo Brito
title Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments
title_short Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments
title_full Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments
title_fullStr Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments
title_full_unstemmed Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments
title_sort risk analysis for autonomous underwater vehicle operations in extreme environments
url https://doi.org/10.1111/j.1539-6924.2010.01476.x
long_lat ENVELOPE(-101.000,-101.000,-75.000,-75.000)
ENVELOPE(-45.900,-45.900,-60.633,-60.633)
geographic Pine Island Glacier
Meier
geographic_facet Pine Island Glacier
Meier
genre Antarc*
Antarctica
Pine Island
Pine Island Glacier
genre_facet Antarc*
Antarctica
Pine Island
Pine Island Glacier
op_relation https://doi.org/10.1111/j.1539-6924.2010.01476.x
op_doi https://doi.org/10.1111/j.1539-6924.2010.01476.x
container_title Risk Analysis
container_volume 30
container_issue 12
container_start_page 1771
op_container_end_page 1788
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