A Markov Chain state transition approach to establishing critical phases for AUV reliability

The deployment of complex autonomous underwater platforms for marine science comprises a series of sequential steps. Each step is critical to the success of the mission. In this paper we present a state transition approach, in the form of a Markov chain, which models the sequence of steps from pre-launch to operation to recovery. The aim is to identify the states and state transitions that present higher risk to the vehicle and hence to the mission, based on evidence and judgment. Developing a Markov chain consists of two separate tasks. The first defines the structure that encodes the sequence of events. The second task assigns probabilities to each possible transition. Our model comprises eleven discrete states, and includes distance-dependent underway survival statistics. The integration of the Markov model with underway survival statistics allows us to quantify the likelihood of success during each state and transition and consequently the likelihood of achieving the desired mission goals. To illustrate this generic process, the fault history of the Autosub3 autonomous underwater vehicle provides the information for different phases of operation. The method proposed here adds more detail to previous analyses; faults are discriminated according to the phase of the mission in which they took place

Results of expert judgments on the faults and risks with Autosub3 and an analysis of its campaign to Pine Island Bay, Antarctica, 2009

Probabilistic risk assessment is a methodology that can be systematically applied to estimate the risk associated with the design and operation of complex systems. The National Oceanography Centre, Southampton, UK has developed a risk management process tailored to the operation of autonomous underwater vehicles. Central to the application of the risk management process is a probabilistic risk assessment. The risk management process was applied to estimate the risk associated with an Autosub3 science campaign in the Pine Island Glacier, Antarctica, and to support decision making. The campaign was successful. In this paper we present the Autosub3 risk model and we show how this model was used to assess the campaign risk

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

India-UK Water Security Exchange Initiative - February 2016 visit. Final report

This report outlines the conclusions of a visit by senior Indian water managers and decision makers to the UK in February 2016. A seven day visit to the UK took place from 13th-20th February 2016, coordinated by the UK Water Partnership and funded by several UK organisations. The report is intended for India-UK Water Security Exchange Initiative participants and stakeholders

Validation of the Ceredigion Youth Screening Tool

Risk assessment practices in Youth Justice in England and Wales have been predominantly conducted through the use of Asset (now Asset Plus), a tool that has shown good predictive capacity and case management properties (Baker, 2014; Wilson & Hinks, 2011). However, recent commentary on the use of such metrics with young people has questioned the longer-term benefits when issues such as the labelling/criminalisation of young people and more diversionary policies are brought to account (Bateman, 2011). Evidence suggests that the majority of young people will not continue to engage in challenging/ anti-social behaviour in the medium-term and only a small minority will actually offend in the longer-term, regardless of any interventions they may receive (Haines and Case, 2015). Hence, being able to ?screen out? this larger low-risk cohort could free up resources, whilst also having a longer-term positive impact upon the individual through a reduction in stigmatisation/ labelling effects. This paper outlines the development of the Ceredigion Youth Screening Tool (CYSTem), developed and tested to address the two facets of criminality and vulnerability and closely aligned to the eight key risk indicators identified in the R-N-R literature (Andrews and Bonta, 2010). Initial results with a cohort of 342 young people indicates good convergent and discriminative validity in screening out the lowest level referrals, whilst also identifying 90% of potential future offenders. More importantly, CYSTem is able to screen out approximately 35% of low risk offenders whom are unlikely to require formal evaluation and/or intervention. Suggestions for future scale refinement and the wider implications of screening out low risk referrals are also discussed.publishersversionPeer reviewe

Understanding the power requirements of autonomous underwater systems, Part I: An analytical model for optimum swimming speeds and cost of transport

Many marine species exhibit capabilities that would be desirable for manmade systems operating in the maritime environment. However, without detracting from the potential, if bioinspiration is to prove beneficial, it is important to have a consistent set of metrics that allow fair comparison, without bias, when comparing the performance of engineered and biological systems. In this study we focus on deriving an unbiased metric of performance applicable to marine animals and engineered subsea vehicles for one of the most fundamental of properties; that of the energy cost of locomotion. We present a rational analytical model of the physics behind the total energy cost of locomotion applicable to both biological and engineered autonomous underwater marine systems. This model proposes the use of an equivalent spheroid efficiency as a fair metric to compare engineered and biological systems. The model is then utilised to identify how changes in mass, speed, spheroid efficiency and hotel load impact the performance of the system