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Bayesian Networks for Predicting Remaining Life

Volume 6, Number 5, September 2010 - Paper 9 - pp. 499-512

YASMINE ROSUNALLY1, STOYAN STOYANOV1, CHRIS BAILEY1, PETER MASON2, SHEELAGH CAMPBELL3, GEORGE MONGER4 and IAN BELL5

1 School of Computing and Mathematical Sciences, University of Greenwich, Greenwich,    London, SE10 9LS, United Kingdom
2 The Cutty Sark Trust, 2 Greenwich Church Street, Greenwich. London, SE10 9BG, United Kingdom
3 Applied Electrochemistry Group, School of Pharmacy and Biomedical Sciences, University of Portsmouth, St Michael's Building, White Swan  Road, Portsmouth, PO1 2DT, United Kingdom
4 Conservation and Museum Services, Unit 6a Glebe Farm Business Units, Woodland Close,  Onehouse, Suffolk, IP14 3HL, United Kingdom
5 Bell Rigging, Conservation Technical Services, 46 Wendover Road, London, SE9 6PA, United Kingdom

(Received on September 30, 2009, revised March 27, 2010) 


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.

 

References: 08

 

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