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

Record type: Article

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.

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Citation

Brito, Mario and Griffiths, Gwyn (2011) A Markov Chain state transition approach to establishing critical phases for AUV reliability IEEE Journal of Oceanic Engineering, 36, (1), pp. 139-149. (doi:10.1109/JOE.2010.2083070).

More information

Published date: 2011
Keywords: Markov processes, Risk Analysis, Underwater vehicles
Organisations: Ocean Technology and Engineering

Identifiers

Local EPrints ID: 69179
URI: http://eprints.soton.ac.uk/id/eprint/69179
ISSN: 0364-9059
PURE UUID: 5862f95b-9992-447c-9c54-e1b18ab46c1f

Catalogue record

Date deposited: 22 Oct 2009
Last modified: 19 Jul 2017 00:13

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