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.

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 largely complex and expensive, demanding an increased amount of both human and technical resources. In this framework, an optimal scheduling of maintenance interventions is an issue of increased relevance. In this work a method for optimization of the tamping scheduling is presented. It is based on a heuristic algorithm, which finds a very detailed tamping schedule where each planned intervention is fully specified. The algorithm tries to maximize an objective function, which is a quantitative expression of the maintenance process's objectives defined by the railway company. It first finds an upper bound for the objective function value, and then returns the best feasible solution found. The method is validated by means of a case study based on real data of the 240 km track of a French high speed TGV line. The results presented show that the value of the best solution found is very near to the upper bound (the difference is smaller than 1%), with a calculation time of under 1 second using a standard computer, so we think the heuristic has a great performance potential.

No abstract

No abstract

Zusammenfassung:In der Praxis werden zur Durchfüh-rung von Zuverlässigkeits-und Sicherheitsanalysen, kurz RAMS-Analysen (Reliability, Availability, Maintainability, Safety) verschiedene Beschreibungsmittel, Methoden und Softwarewerkzeuge eingesetzt. Mit Hilfe von PetrinetzModellen können jedoch alle vier Eigenschaften anhand nur eines einzigen Modells bewertet werden. In diesem Beitrag wird das Modellierungs-und -analysewerkzeugTool für stochastische Petrinetze präsentiert, welche hierfür mit seinen umfangreichen Modellierungs-und Analysefunktionalitäten eine hervorragende Möglichkeit anbietet. Schlüsselwörter: RAMS, Petrinetz, Modelierung, Analyse.Abstract: Nowadays several description means, methods and software tools are used to perform RAMS (Reliability, Availability, Maintainability, Safety) analysis. With the help of Petri nets, all four properties can be analysed using just one unique model. However, modelling real systems by means of simple Petri nets often turns to be cumbersome. Such an approach can be outstandingly put into practice using -Tool, the Petri net modelling and analysis tool for stochastic petrinets is hereby presented.