A track ballast maintenance and inspection model for a rail network

Darren Prescott and John Andrews

Journal of Risk and Reliability, 2013, vol. 227, issue 3, 251-266

Abstract: The geometry of railway track governs the quality of the ride for passengers and, should it deteriorate to an extreme state, can become a safety concern with potential derailment. The geometry is dependent upon a number of different factors, among which is the condition of the ballast. Several options exist to control the condition of the ballast, including manual intervention, tamping and stoneblowing. Ballast condition is monitored implicitly by measuring the geometry of the railway lines using a specially equipped measurement train. By analysing the data collected by this train, the deterioration process of the railway geometry can be understood. Using this understanding, mathematical models can then be constructed, which take account of the possible maintenance and renewal options to predict the track state. This model enables decisions to be made on the best or optimal strategy for maintenance and renewal of the ballast. This article describes a model of the track maintenance process for a railway network. Owing to their flexibility, the model is formulated using a Petri net and combines the deterioration, maintenance and inspection processes for a railway network containing a number of regions. There are a limited number of maintenance machines in the network and the maintenance in each region is organised independently. The model also takes account of the fact that the maintenance of track sections with severe levels of ballast deterioration must take priority over all other maintenance in the network and allows for opportunistic maintenance to be analysed.

Keywords: Railway network; asset management; life cycle analysis; Petri nets (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:227:y:2013:i:3:p:251-266

DOI: 10.1177/1748006X13482848

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