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Generic Bayesian network models for making maintenance decisions from available data and expert knowledge

Zhang, H; Marsh, DWR; (2018) Generic Bayesian network models for making maintenance decisions from available data and expert knowledge. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability , 232 (5) pp. 505-523. 10.1177/1748006X17742765. Green open access

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Abstract

To maximise asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Various statistical models have been proposed for predicting this, but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how (1) data on the condition of assets available from their periodic inspection can be used, (2) failure data from related groups of asset can be combined using judgement from experts and (3) expert knowledge of the deterioration’s causes can be combined with statistical data to adjust predictions. A case study of bridges on the rail network in Great Britain (GB) is presented, showing how the model could be used for the maintenance decision problem, given typical data likely to be available in practice.

Type: Article
Title: Generic Bayesian network models for making maintenance decisions from available data and expert knowledge
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/1748006X17742765
Publisher version: https://doi.org/10.1177%2F1748006X17742765
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bayesian network, available data, expert knowledge, maintenance modelling, deterioration, GB rail bridges
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10086850
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