A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions
A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions
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DOI：
10.1016/j.ress.2015.10.004
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发表时间：
2016-02-01
影响因子：
8.1
通讯作者：
Griffiths, Gwyn
中科院分区：
文献类型：
--
作者：
Brito, Mario;Griffiths, Gwyn
自主水下航行器（auv）是科学研究和监测以及军事和商业数据收集目的的有效平台。但是，在执行任何任务期间都不可避免地有损失的危险。由于车辆可靠性和环境因素的结合，损失风险的量化是复杂的，不能仅通过分析手段确定。另一种方法——正式的专家判断——是一个耗时的过程；因此，需要一种方法来扩大判断的适用性，使其超出对特定环境的启发的狭窄范围。我们提出并探索了一种基于贝叶斯信念网络（BBN）的解决方案，其中专家判断的结果被作为由于失败而导致损失的初始先验概率。网络拓扑分别捕获环境对车辆和支持平台的因果影响，并将它们结合起来，以产生故障造成损失的最新概率。然后使用Kaplan-Meier估计器的扩展版本来更新飞行距离的任务风险概况。提出了BBN的敏感性分析，并详细讨论了Autosub3 AUV在阿蒙森海部署的案例研究。(C) 2015 Elsevier Ltd.版权所有。
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. (C) 2015 Elsevier Ltd. All rights reserved.