Generic Bayesian network models for making maintenance decisions from available data and expert knowledge. (October 2018)

• Record Type: Journal Article
• Authors: Zhang, Haoyuan; Marsh, D William R
• Other Names: Revie Matthew guest-editor.; Walls Lesley guest-editor.; Bedford Tim guest-editor.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 5(2018:Oct.)
• Page Start: 505
• Page End: 523
• Publication Date: 2018-10
• Subjects: Bayesian network -- available data -- expert knowledge -- maintenance modelling -- deterioration -- GB rail bridges; Reliability (Engineering) -- Mathematical models -- Periodiclals; Risk assessment -- Mathematical models -- Periodicals; Engineering design -- Mathematical models -- Periodicals; 620.00452
• DOI: 10.1177/1748006X17742765
• Languages: English
• ISSNs: 1748-006X

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.