Bayesian networks for critical maintenance decisions on the railway network

An important recent paper (published in the Journal of Risk and Reliability) by Haoyuan Zhang and William Marsh of Queen Mary University of London presents a Bayesian network model that can be used for maintenance decision support that is especially relevant for rail safety. The model overcomes the practical limitations of previous statistical models that have attempted to maximise asset reliability cost-effectively, by scheduling maintenance based on the likely deterioration of an asset. 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 
3. expert knowledge of the causes of deterioration can be combined with statistical data to adjust predictions. 

The model (which was developed using the AgenaRisk software) is applied to a case study of bridges on the rail network in the UK.

A full pre-publication version is available here.

The full publication details for the paper are:

Zhang, H., & R Marsh, D. W. (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), 505–523. https://doi.org/10.1177/1748006X17742765