Tracking effectiveness of risk mitigation for autonomous underwater vehicles

1995年12月，一组海洋科学家和技术专家举行会议，确定一项专题方案提案的范围，该方案提案将展示自主水下航行器对海洋科学的效用。该方案的目的是解决只能利用这类车辆的独特功能才能回答的问题。“自动潜水器科学任务”方案资助了自动潜水器1号、自动潜水器2号和自动潜航器3号的自主水下机器人的开发。今天，这一愿景正在海洋2025科学计划中实现，在该计划中，NERC目前正在资助Autosub6000和Autosub Long Range的开发--这里的目标是更深入、更长远。这样的平台将有助于英国保持其作为世界海洋科学领导者之一的地位。评估AUV的可靠性对于在危险和复杂环境中的部署至关重要。可靠性是指系统在规定的环境条件下，在规定的一段时间内执行规定功能的概率。AUV的可靠性受到许多因素的影响，人为错误起着关键作用，但其他挑战也来自恶劣的操作条件，一些AUV组件没有设计成在这种条件下运行，此外，车辆的一些组件也没有设计成一起工作。因此，我们不能忽视这样一个事实，即AUV可靠性的测量必须基于专家的主观风险评估。如果我们要使用专家风险评估，我们必须遵循一个正式的程序。正式的启发过程将使评估具有透明度和可重复性。为了解决这个问题，水下系统实验室(USL)创建了一个针对AUV操作的风险和可靠性管理流程(RMP-AUV)。该项目的目的是验证现有的方法，并开发新的、更详细的方法来估计AUV的操作风险。新的风险模型将量化故障或事故缓解对损失风险估计和无法交付数据风险的影响。其目的是建立新的模型来衡量AUV的可靠性增长。新方法将基于贝叶斯统计。这是一种数学方法，在这种方法中，对一个命题的先前信念--在我们的例子中，是风险估计--是基于该命题受到新观察影响的可能性而更新的。通过与AUV制造商国际潜艇工程(ISE)和加拿大国防研究与发展公司(DRDC)的合作，我们有一个难得的、有时间限制的机会，通过ISE探索者AUV使用关于故障和事件的广泛数据集。此外，我们的合作伙伴渴望在其高影响力项目中共同开发、测试和应用风险缓解跟踪和建模方法，以支持加拿大提交的《海洋法公约》第76条。这些模型将用已经收集的可靠性数据进行测试，跟踪2010年4月北极行动、工程返工和2011年北极探险中的故障。

In December 1995 a group of marine scientists and technologists met to define the scope of a thematic programme proposal that would demonstrate the utility of Autonomous Underwater Vehicles (AUVs) for ocean science. The aim of the programme was tackle questions that could only be answered using the unique features of such vehicles. The 'Autosub Science Missions' programme funded the development of Autosub1, Autosub2 and Autosub3 autonomous underwater vehicles. Today, this vision is pursued in Oceans2025 science programme, in which NERC is currently funding the development of Autosub6000 and Autosub Long Range - the aim here is to go deeper and longer. Such platforms will help the U.K. maintain its position as one of the World's leaders in ocean science. Estimating AUV reliability is of paramount importance for deployments in hazardous and complex environments. Reliability is the probability that a system will perform its specified function over a given period of time under defined environmental conditions. The AUV reliability is influenced by many factors, human errors play a critical role but other challenges arise from severe operational conditions, the fact that some AUV components were not designed to operate in such conditions and furthermore the fact that some of the vehicle's components were not designed to work together. As a result, we cannot ignore the fact that measuring AUV reliability must be based experts' subjective risk assessment. If we are going to use experts risk assessment we must follow a formal process. A formal elicitation process will enable transparency and repeatability of the assessment. To tackle this problem, the Underwater Systems Laboratory (USL) created a risk and reliability management process tailored to AUV operations (RMP-AUV). This project is aimed to validate existing methods and to develop new, more detailed methods for estimating AUV operational risk. The new risk models will quantify the effects of fault or incident mitigation on estimates of risk of loss and on risk of non-delivery of data. The aim is to derive new models to measure the reliability growth of AUVs. The new methods will be based on Bayesian statistics. This is a mathematical method in which the prior belief in a proposition -in our case, risk estimate- is updated based on the likelihood that the proposition is affected by a new observation. Through collaboration with AUV manufacturer International Submarine Engineering (ISE) and Defence Research and Development Canada (DRDC) we have a rare, time-limited opportunity to use an extensive data set on the faults and incidents with an ISE Explorer AUV. Furthermore, our partners are eager to co-develop, test and apply risk mitigation tracking and modelling methods within their high impact project in support of Canada's UNCLOS Article 76 submission. The models would be tested with reliability data already gathered, with tracking of faults from the April 2010 Arctic campaign, engineering rework, and a 2011 Arctic expedition.