Railway bridge fault detection using Bayesian belief network

Vagnoli, M. and Remenyte-Prescott, R. and Andrews, J. (2017) Railway bridge fault detection using Bayesian belief network. In: Stephenson Conference: Research for Railways, 25th - 27th April 2017, London, United Kingdom.

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Abstract

Bridges are one of the most critical structures of the railway system. External loads may affect the bridge health state, and consequently their safety, availability and reliability can be improved by monitoring their condition and planning maintenance accordingly. In this paper, a Bayesian Belief Network (BBN) fault detection methodology for a truss steel railway bridge is proposed. The BBN is developed to assess the health state of the whole bridge using evidence about the behaviour of the bridge. In this initial study, the evidence is provided in terms of the values of displacement computed by a Finite Element model.

Item Type: Conference or Workshop Item (Paper)
Schools/Departments: University of Nottingham, UK > Faculty of Engineering
Depositing User: Eprints, Support
Date Deposited: 08 Mar 2017 11:44
Last Modified: 18 Oct 2017 18:54
URI: http://eprints.nottingham.ac.uk/id/eprint/41151

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