Monte Carlo simulation of railway track geometry deterioration and restoration

Abstract

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.

Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models

“…In fact, specialized literature usually does not cover this improvement in great detail and only a few references have been found, such as [11] and [13]. A recent work by Quiroga and Schnieder [14] has used accumulated tamping interventions as an explaining variable, showing higher variances for higher number of accumulated tamping interventions. Furthermore, a recent work by Vale and Lurdes [16] also discussed a stochastic model for the geometrical railway track degradation process, focusing on the standard deviation of longitudinal level defects and not on the standard deviation of horizontal alignment.…”

A heuristic approach to railway track maintenance scheduling

“…The process model used for forecasting is the one presented in [2]. It relies on 2 assumptions, namely:…”

“…Eventually, it could also be that interventions are not registered in the database. The details of how this influences the model can be found in [2]. It can be shortly described as the addition of stochastic noise to represent the measurement errors, and the assumption of interventions when the negative increment is greater than a certain threshold.…”

Spatial characteristics of probability distribution of longitudinal irregularity in ballasted track

“…Most research has provided statistical analyses of the probability distribution functions, which different track irregularity characteristic values of different lengths of segments obeyed [1,2]. Some studies have established deterministic models to predict the deterioration of longitudinal irregularity [3][4][5], and, since 2010, stochastic restoration models, based on the distribution function characteristics of track irregularity characteristic values, have begun to be established [6][7][8][9][10][11][12]. However, most existing literature has used the same distribution function to describe the characteristics of probability distribution of irregularity and have ignored the difference in probability distribution fitting, which is caused by different line types.…”