Quiroga, L. M.; Schnieder, E.:
Monte Carlo simulation of railway track geometry deterioration and restoration.
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. , Dezember 2011.


Travelling safely and comfortably on high-speed railway lines requires excellent conditions of the whole railway infrastructure in general and of the railway track geometry in particular. The maintenance process required to achieve such excellent conditions is complex and expensive, demanding a large amount of both human and technical resources. In this framework, choosing the right maintenance strategy becomes a critical issue. A reliable simulation of the railway geometry ageing process would offer a great advantage for the optimization of planning and scheduling of maintenance activities. A fundamental requirement for such simulation is a statistical model describing the behaviour of the railway track geometry deterioration as well as the effects of maintenance activities. The French railway operator SNCF has been periodically measuring the geometrical characteristics of its high-speed network since its commissioning (i.e. for more than 20 years now). These records are an excellent data source to achieve a sound statistical description of the process.

In this paper a new system identification method to obtain such simulations is presented. The proposed method uses a grey-box model: a model structure and its constraints are specified basing on previous knowledge of the process to be identified, and then the set of parameter values which best fits the signal measurements is searched. As previous knowledge indicates that the process is non-linear, parameter values are searched by means of the Levenberg–Marquardt algorithm, an iterative technique that finds a local minimum of a function that is expressed as the sum of squares of non-linear functions. Furthermore, the presented model is extended in order to analyse the effect of the variation of factors influencing the ageing process (e.g. operational speed). Finally, the method is applied and validated with real data of a French high-speed TGV line.