No abstract

Signalling systems ensure the safe operation of the railway network. Their reliability and maintainability directly affect the capacity and availability of the railway network, in terms of both infrastructure and trains, as a line cannot be fully operative until a failure has been repaired. The purpose of this paper is to propose a data-driven decision support model that integrates the various parameters of corrective maintenance data and to study maintenance performance by considering different reliability, availability, maintainability and safety parameters. This model is based on failure analysis of historical events in the form of corrective maintenance actions. It has been validated in a case study of railway signalling systems and the results are summarised. The model allows the creation of maintenance policies based on failure characteristics, as it integrates the information recorded in the various parameters of the corrective maintenance work orders. The model shows how the different failures affect the dependability of the system: the critical failures indicate the reliability of the system, the corrective actions give information about the maintainability of the components, and the relationship between the corrective maintenance times measures the efficiency of the corrective maintenance actions. All this information can be used to plan new strategies of preventive maintenance and failure diagnostics, reduce the corrective maintenance and improve the maintenance performance.

Railway vehicles are efficient because of the low resistance in the contact zone between wheel and rail. In order to remain efficient, train operators and infrastructure owners need to keep rails, wheels and vehicles in an acceptable condition. Wheel wear affects the dynamic characteristics of vehicles and the dynamic force impact on the rail. The shape of the wheel profile affects the performance of railway vehicles in different ways. Wheel condition has historically been managed by identifying and removing wheels from service when they exceed an impact threshold. Condition monitoring of railway vehicles is mainly performed using wheel impact load detectors and truck performance detectors. These systems use either forces or stress on the rail to interpret the condition. This paper will show measurements taken at the research station outside Luleå in northern Sweden. The station measures the wheel/rail forces, both lateral and vertical, at the point of contact in a curve with a 484 m radius at speeds of up to 100 km/h. Data are analysed to show differences for various wheel positions and to determine the robustness of the system.

Turnouts are critical components of track systems in terms of safety, operation and maintenance. Each year, a considerable part of the maintenance budget is spent on their inspection, maintenance and renewal. Applying a cost-effective maintenance strategy helps to achieve the best performance at the lowest possible cost. In Sweden, the geometry of turnouts is inspected at predefined time intervals using the STRIX / IMV 100 track measurement car. This study uses time series for the measured longitudinal level of turnouts on the Iron Ore Line (Malmbanan) in northern Sweden. Two different approaches are applied to analyse the geometrical degradation of turnouts due to dynamic forces generated by train traffic. In the first approach, the recorded measurements are adjusted at the crossing point and then the relative geometrical degradation of turnouts is evaluated by using two defined parameters, the absolute residual area and the maximum settlement, In the second approach, various geometry parameters are defined to estimate the degradation in each measurement separately. The growth rate of the longitudinal level degradation as a function of million gross tonnes / time is evaluated. The proposed methods are based on characterisation of the individual track measurements. The results facilitate correct decision-making in the maintenance process through understanding the degradation rate and defining the optimal maintenance thresholds for the planning process. In the long run, this can lead to a cost-effective maintenance strategy with optimised inspection and maintenance intervals.