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Bayesian networks for predicting remaining life

Bayesian networks for predicting remaining life

Rosunally, Yasmine, Stoyanov, Stoyan ORCID: 0000-0001-6091-1226, Bailey, Christopher, Mason, Peter, Campbell, Sheelagh, Monger, George and Bell, Ian (2010) Bayesian networks for predicting remaining life. International Journal of Performability Engineering, 6 (5). pp. 499-512. ISSN 0973-1318

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Abstract

The Cutty Sark is undergoing major conservation to slow down the deterioration of the original Victorian fabric of the ship. While the conservation work being carried out is "state of the art", there is no evidence at present of the effectiveness of the conservation work 50 plus years ahead. A Prognostics Framework is being developed to monitor the "health" of the ship's iron structures to help ensure a 50 year life once conservation is completed with only minor deterioration taking place over time. The framework encompasses four approaches: Canary and Parrot devices, Physics-of-Failure (PoF) models, Precursor Monitoring and Data Trend Analysis and Bayesian Networks. Bayesian network models are used to update remaining life predictions from PoF models with information from precursor monitoring. This paper presents the prognostics framework with focus on the Bayesian network approach used to improve remaining life predictions of Cutty Sark iron structures.

Item Type: Article
Uncontrolled Keywords: prognostics framework, Bayesian networks, computer modelling, Cutty Sark, conservation, physics-of-failure,
Subjects: Q Science > QA Mathematics
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Mechanics & Reliability Group
School of Computing & Mathematical Sciences > Department of Computer Science
School of Computing & Mathematical Sciences > Department of Computer Systems Technology
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:11
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/4513

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