A railway track asset management strategy must ensure that the condition of the track stays at an acceptable level, ensuring passenger comfort and safety. The determination of an efficient and effective strategy is a complex problem that requires consideration of the interrelated processes of deterioration, inspection, maintenance and renewal. Railway traffic causes the track’s geometry to deteriorate through time. When the track’s geometry has been assessed and seen to have deteriorated, maintenance is scheduled to restore the track’s geometry using a tamping machine. Tamping involves lifting the track to the required position before inserting vibrating tines into the ballast either side of the sleepers and using them to pack the ballast below. This process causes damage to the ballast, causing a relative acceleration in future track geometry deterioration and decreasing the time between subsequent maintenance interventions. This paper describes a Markov model that can be used to investigate the asset management strategy applied to a railway track section. The model predicts the way that the track section’s condition changes with time for a given asset management strategy, which is defined through the specification of a number of model parameters. The model is applied to a track section with a specified maintenance strategy and is used to investigate the track’s performance for that strategy. Parameters corresponding to asset management decisions that relate to inspection, maintenance and renewal are then changed in order to illustrate the effects of the decisions on the track’s life cycle. The Markov model provides a simple yet powerful means of investigating the effects of an asset management strategy on a railway track section.
Investigating railway track asset management using a Markov analysis
Proceedings of the Institution of Mechanical Engineers, Part F (Journal of Rail and Rapid Transit) ; 229 , 4 Special Section
2015
15 Seiten, 15 Quellen
Conference paper
English
Investigating railway track asset management using a Markov analysis
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