Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments

Abstract

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.

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ORCID for M. Brito: orcid.org/0000-0002-1779-4535

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