1,721,061 research outputs found
On the reliability of expert’s assessments for autonomous underwater vehicle risk of loss prediction: are optimists better than pessimists?
No full textExpert judgment elicitation is a key element of formal risk assessment. Some research in this subject has focused on identifying the best way to aggregate expert judgments. In this paper we explore this problem. Given the divergence in expert judgments, when using mathematical aggregation it is possible to group expert judgments according to their mood, this is optimists and pessimists. Using hard data, gathered after the expert judgment elicitation process we test which group performs better. In this paper, we group the expert judgments elicited for building the risk model for the Nereid-UI hybrid autonomous underwater vehicle into optimists and pessimists. After the risk assessment the vehicle conducted 16 missions. We compared the two risk models from the risk assessment against the observed risk from actual missions. Our results showed that differences between the pessimist risk model estimates and the observed risk are not statistically significant. On the other hand differences between the predicted risk using the optimistic risk model and the observed risk are statistically significant. We conclude that for early missions in extreme environment it is imperative to use the pessimistic risk model estimates to inform decision making
Investigation into the lack of communication from the timed release data pods from the MYRTLE-X Lander deployed at EBHi in 2015: Decision Tree Analysis
No full textMYRTLE-X is a deep ocean instrument platform design and developed by the National Oceanography Centre. This system comprises a number of innovative components, which will enable transformation of the way ocean observation data are obtained. MYRTLE-X was deployed as part of the RAPID telemetry system on the 29th of October 2015, as part of the RAPID array programme. The system comprised an acoustic link from a mooring to a nearby lander frame, which had eight pods. Seven of the pods were expected to be released at a pre-defined time during the 18-month deployment of the RAPID array. Following release to the surface, the pods were expected to transfer data to the on-shore station. The first three releases were scheduled for January, March and June 2016 but no communication has been received from any of these pods. This report presents the results of the decision tree analysis into the possible root causes for the lack of communication from the MYRTLE-X pods. The investigation meeting took place on June 8th, 2016<br/
Towards building a safety case for Marine Unmanned Surface Vehicles: a Bayesian perspective
Full text linkMarine Unmanned Surface Vehicles (MUSVs) are essential platforms for persistent and adaptable ocean monitoring and sampling. In order to operate these platforms in coastal areas or near oil and gas waters the MUSVs must meet statutorily and industry safety requirements. Given the novelty of these platforms, there is lack of evidence to support the claim that a given safety target can be met without any additional protection. Therefore, for safety critical operations, MUSVs require the implementation of a safety function. The development of a safety function must comply with IEC61508 safety standard, which requires a quantification of the safety integrity level. Compliance to IEC61508 is subject to subjective uncertainty. The nature of the technology in terms of mode of operation and the environment in which operates exacerbates this uncertainty. This paper presents a Bayesian belief network for formalizing the safety arguments underpinning MUSV compliance to IEC 615078 safety standard
A Bayesian approach to predicting risk of loss during Autonomous Underwater Vehicle missions
Full text linkAutonomous 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
Predicting risk in missions under sea ice with Autonomous Underwater Vehicles
No full textAutonomous Underwater Vehicles (AUVs) have afuture as effective platforms for multi-disciplinary scienceresearch and monitoring in the polar oceans. However, operationunder ice may involve significant risk to the vehicle. A riskassessment and management process that balances the riskappetite of the responsible owner with the reliability of thevehicle and the probability of loss has been proposed. A criticalstep in the process of assessing risk is based on expert judgmentof the fault history of the vehicle, and what affect faults orincidents have on the probability of loss. However, this subjectiveexpert judgment is sensitive to the nature of sea ice cover. Incontrast to the simple, yet high risk, case of operation under anice shelf, sea ice offers a complex risk environment. Furthermore,the risk is modified by the characteristics of the support vessel,especially its ice-breaking capability. We explore how theASPeCt sea ice characterization protocol and probabilitydistributions of ice thickness and concentration can be usedwithin a rigorous process to quantify risk given a range of sea iceconditions and with ships of differing ice capabilities. A solutionfounded on a Bayesian Belief Network approach is proposed,where the results of the expert judgment elicitation is taken as areference. The design of the network topology captures the causaleffects of the environment separately on the vehicle and on theship, and combines these to produce the output. Complementaryexpert knowledge is included within the conditional probabilitytables of the Bayesian Belief Network. Using expert judgment onthe fault history of the Autosub3 vehicle and sea ice datagathered in the Arctic and Antarctic by its predecessor,Autosub2, examples are provided of how risk is modified by thesea ice environment.<br/
Updating autonomous underwater vehicle risk based on the effectiveness of failure prevention and correction
No full textAutonomous underwater vehicles (AUVs) have proven to be feasible platforms for marine observations. Risk and reliability studies on the performance of these vehicles by different groups show a significant difference in reliability, with the observation that the outcomes depend on whether the vehicles are operated by developers or non-developers. We show that this difference in reliability is due to the failure prevention and correction procedures - risk mitigation - put in place by developers. However, no formalisation has been developed for updating the risk profile based on the expected effectiveness of the failure prevention and correction process. In this paper we present a generic Bayesian approach for updating the risk profile, based on the probability of failure prevention and correction and the number of subsequent deployments on which the failure does not occur. The approach, which applies whether the risk profile is captured in a parametric or nonparametric survival model, is applied to a real case study of the ISE Explorer AUV
Proceedings of the 1st International Workshop on Energy Transition to Net Zero Energy Reliability, Risk, and Resilience (ETZE R3)
No full textThe International Workshop on Energy Transition to Zero Carbon: Reliability, Risk, and Resilience is a joint effort by the B. John Garrick Institute for the Risk Sciences at the University of California Los Angeles (UCLA-GIRS) and the European Safety and Reliability (ESREL) Conference.ETZE R3 is a ESREL workshop consist of multiple sessions designed to be a platform for cross-industrial and interdisciplinary effort and knowledge exchange on risk and resilience of energy transition technologies to net zero.The workshop gathers experts from academia, industry, and regulatory agencies to discuss challenges and potential solutions for energy transition technologiesto net zero from different perspectives.This workshop complements existing sessions and workshops organised around specific types of net zero energy risk and resilience assessment. ETZE R3 distinguishes itself from these events by addressing the energy transition issues and risk and resilience topics together and proposing possible solutions for safe and reliable transition to net zero energies.ETZE R3-2023 was held at the University of Southampton, United Kingdom, on 3rd-7th September 2023, and gathered 77 participants from 34 organisations from around the globe. This report summarises ETZE R3-2023 workshop. It provides an overview of the main points raised by a community of experts on the current status of risk issues of energy transition to net zero methods. It also outlines research directions for safer, more reliable and resilient net zero technologies
Uncertainty management during hybrid autonomous underwater vehicle missions
No full textEffective autonomous underwater vehicle (AUV) mission uncertainty management requires identifying optimal mission profile based on multiple uncertain attributes. The new deep underwater glider being developed by the BRIDGES consortium will enable the use of low power AUV to sample three different phenomena: living things, sea mining and oil and gas exploration. In order to have the versatility to address these three requirements, the underwater glider will operate in a hybrid mode, using a buoyancy change and propeller based propulsion system. When we developed a failure model for this AUV we realized that different risk profiles are obtained if we are using different type of propulsion system. Therefore, we propose a multiple attribute utility technique to inform the optimal mission profile in light of mission risks and opportunities
A Systems Dynamics Framework for Risk Management of Multiple Autonomous Underwater Vehicles
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