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

Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and 9 monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of 10 loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability 11 and environmental factors, and cannot be determined through analytical means alone. An alternative 12 approach – formal expert judgment – is a time-consuming process; consequently a method is needed to 13 broaden the applicability of judgments beyond the narrow confines of an elicitation for a defined 14 environment. We propose and explore a solution founded on a Bayesian Belief Network (BBN), where the 15 results of the expert judgment elicitation are taken as the initial prior probability of loss due to failure. The 16 network topology captures the causal effects of the environment separately on the vehicle and on the 17 support platform, and combines these to produce an updated probability of loss due to failure. An extended 18 version of the Kaplan Meier estimator is then used to update the mission risk profile with travelled distance. 19 Sensitivity analysis of the BBN is presented and a case study of Autosub3 AUV deployment in the Amundsen 20 Sea is discussed in detail.