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 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.

[1]  Mario P. Brito,et al.  Eliciting expert judgment on the probability of Loss of an AUV operating in four environments , 2008 .

[2]  Paul H. Garthwaite,et al.  Measurement of Sea-ice draft using upward-looking ADCP on an autonomous underwater vehicle , 2006, Annals of Glaciology.

[3]  Maria Hänninen,et al.  Influences of variables on ship collision probability in a Bayesian belief network model , 2012, Reliab. Eng. Syst. Saf..

[4]  Gwyn Griffiths,et al.  Measurements beneath an Antarctic ice shelf using an autonomous underwater vehicle , 2006 .

[5]  H Otway,et al.  Expert judgment in risk analysis and management: process, context, and pitfalls. , 1992, Risk analysis : an official publication of the Society for Risk Analysis.

[6]  Daniel Zelterman,et al.  Bayesian Artificial Intelligence , 2005, Technometrics.

[7]  Gwyn Griffiths,et al.  Towards a risk management process for automous underwater vehicles , 2007 .

[8]  Jeremy E. Oakley,et al.  Uncertain Judgements: Eliciting Experts' Probabilities , 2006 .

[9]  Ralph L. Keeney,et al.  Eliciting probabilities from experts in complex technical problems , 1991 .

[10]  M. Kynn.  The ‘heuristics and biases’ bias in expert elicitation , 2007 .

[11]  Stephen D. McPhail,et al.  Observations beneath Pine Island Glacier in West Antarctica and implications for its retreat , 2010 .

[12]  J. E. Strutt,et al.  Report of the inquiry into the loss of Autosub2 under the Fimbulisen , 2006 .

[13]  David Wright,et al.  The Use of Multilegged Arguments to Increase Confidence in Safety Claims for Software-Based Systems: A Study Based on a BBN Analysis of an Idealized Example , 2007, IEEE Transactions on Software Engineering.

[14]  Ian Jenkinson,et al.  Modelling dwelling fire development and occupancy escape using Bayesian network , 2013, Reliab. Eng. Syst. Saf..

[15]  James G Bellingham,et al.  Robotics in Remote and Hostile Environments , 2007, Science.

[16]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[17]  A. Tversky,et al.  Subjective Probability: A Judgment of Representativeness , 1972 .

[18]  Peter Wadhams,et al.  A new view of the underside of Arctic sea ice , 2006 .

[19]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[20]  Gwyn Griffiths,et al.  Risk Analysis for Autonomous Underwater Vehicle Operations in Extreme Environments , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[21]  G. Griffiths,et al.  Predicting risk in missions under sea ice with Autonomous Underwater Vehicles , 2008, 2008 IEEE/OES Autonomous Underwater Vehicles.

[22]  Gwyn Griffiths,et al.  Masterclass in AUV Technology for Polar Science , 2009 .

[23]  Anders L. Madsen,et al.  The Hugin Tool for Probabilistic Graphical Models , 2005, Int. J. Artif. Intell. Tools.

[24]  M. J. Cushing,et al.  Comparison of electronics-reliability assessment approaches , 1993 .