Grinding is regularly conducted on railway tracks to prevent crack propagation and surface deterioration. However, grinding can introduce roughness on rail surfaces, potentially leading to stress and strain concentration that increase the likelihood of crack initiation. This paper proposes the utilization of surface roughness obtained by replicating the ground rail surface to assess its impact on train wheel-rail interactions. A novel approach which integrates the replicated roughness into an elastoplastic contact model, allows for a detailed assessment of its effects on contact pressure, and residual strain and stress distributions. The findings highlight the importance of considering surface roughness in predictive maintenance planning for railway infrastructure, as it can significantly influence the structural integrity and long-term performance of the track system.

Infrastructure owners monitor changes in the track geometry to safeguard operation and to plan maintenance activities. Usually, track geometry is monitored using specialized measurement vehicles assessing the track several times a year to establish information on the development of a specific locations along the track. In this paper, a prediction tool is proposed and described that combines information from the measurement vehicles with measurements of onboard monitoring systems on regular trains to estimate and predict the properties longitudinal level and twist. Further, static asset configuration and information on the infrastructure is used in the decision making to provide actionable insights. The tool provides an improved resolution in time for track geometry properties and predictions on how these properties develop in the future including information on the uncertainties.

It will be discussed how data from different sources with irregular sampling need to be preprocessed to be combined and harmonized. Moreover, in what way different principles from data science, machine learning and estimation theory can be combined with domain knowledge to enable a better analytics and decision making. To support engineers, it is shown how a decision support tool for maintenance can be tailored and used. Finally, the tool and approach are showcased and benchmarked on track systems in Europe, where both onboard monitoring data and data from measurement trains is available. The results indicate that such a tool provide improved actionable insights to practitioners.

Globally, railway is experiencing a major technology transformation (or paradigm shift), triggered by the enhanced utilisation of digital technology. This technological transformation affects not only the technical systems, i.e. railway infrastructure and rolling stock, but also regulations, organisations, processes,and individuals. Hence, hardware, software, but also liveware (i.e. humans) are affected. Today, the digitalisation of railway is characterised by digital services. There are also a range of challenges, e.g. data acquisition,transformation, modelling, processing, visualisation, safety, security, quality, and information assurance. To deal with these challenges, the railway industry needs to define strategies, which enable a smooth transformation of the existing configuration to a digitalised system. Digital railway requires a holistic change management approach based on system-of-systems thinking and a set of appropriate technologies and methodologies. The railway digitalisation strategy should be based on systematic risk management that address aspects of, e.g., information security, traffic safety and project risk. In addition, managing changes for a digitalised railway effectively and efficiently also requires a framework for aspects such as needs finding, requirement identification, and impact of changes for individual, teams and organisation. In this work a major case studywithin the ePilot, has been performed in context of the operation and maintenance processes of the Swedish railway. Therefore, this paper aims to propose a framework for implementing innovations and driving change in a digitalised railway.

A framework is presented to evaluate the safety and availability of the railway operation, and quantifying the probability of the signalling system not to supervise the railway traffic. Since a failure of the signalling systems still allows operation of the railway, it is not sufficient to study their effect on the railway operation by considering only the failures and delays. The safety and availability are evaluated, handling both repairs and replacements by using a Markov model. The model is verified with a case study of Swedish railway signalling systems with different scenarios. The results show that the probability of being in a state where operation is possible in a degraded mode is greater than the probability of not being operative at all, which reduces delays but requires other risk mitigation measures to ensure safe operation. The effects that different improvements can have on the safety and availability of the railway operation are simulated. The results show that combining maintenance improvements to reduce the failure rate and increase the repair rate is more efficient at increasing the probability of being in an operative state and reducing the probability of operating in a degraded state.

In the past, railway maintenance actions were usually planned based on the knowledge and experience of the infrastructure owner. The main goal was to provide a high level of safety, and there was little concern about economic and operational optimisation issues. Today, however, a deregulated competitive environment and budget limitations are forcing railway infrastructures to move from safety limits to cost-effective maintenance limits to optimise operation and maintenance procedures. By so doing, one widens the discussion to include both operational safety and cost-effectiveness for the whole railway transport system. In this study, a cost model is proposed to specify the cost-effective maintenance limits for track geometry maintenance. The proposed model considers the degradation rates of different track sections and takes into account the costs associated with inspection, tamping, delay time penalties, and risk of accidents due to poor track quality. It draws on track geometry data from the Iron Ore Line (Malmbanan) in northern Sweden, used by both passenger and freight trains, to estimate the geometrical degradation rate of each section. The methodology is based on reliability and cost analysis and facilitates the maintenance decision-making process to identify cost-effective maintenance thresholds.

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 which integrates the various parameters of corrective maintenance data and to study maintenance performance by considering different RAMS 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.

The Swedish ore line, began in 1898 and was completed and operational by 1902. The line was later electrified and remains so to this day. A study on wheel-rail interaction, to optimize wheel and rail profiles on the Malmbanan and Ofoten lines states that there is a need to develop and apply limit values for the allowable length of single and multiple cracks on wheels. Herein, all stakeholders in the railway organization have to be involved. Provisioning of an integrated decision support system for track and vehicle contributes to increased efficiency and improved effectiveness of the maintenance process, which in turn enables achievement of business excellence. However, an integrated decision support process for maintenance is highly dependent on appropriate information logistics which enables information provisioning to various maintenance stakeholders. One emerging approach for development of eMaintenance solutions is utilization of cloud-based technologies. Cloud-based eMaintenance solutions promises smooth information logistics for maintenance decision support. Hence, the purpose of this paper is to propose an approach for condition-based maintenance decision-making relating to railway vehicle wheels, based on a cloud-based eMaintenance solution. The paper also demonstrates how the proposed approach can be implemented for estimation of Remaining Useful Life (RUL) of railway vehicle wheels.

Due to the more or less fixed inherent capacity of a railway system, capacity consuming events like failures within a railway network should be kept to a minimum. This could be achieved by the use of existing and new condition monitoring systems which can detect, report and predict failure events in an early stage. Demands for higher service quality, higher capacity, network availability and track quality together with less human intervention on tracks, drive the development of railway condition monitoring systems.Failure driven capacity consumption due to worn or defected rolling stock wheels have a big impact on the capacity and the infrastructure condition. Wheel defects such as out-of-round wheels, generates high forces, and could result in large capacity consumption especially for areas with cold climate conditions. Bad wheels cause even higher track wear that reduce the life length of the track. Wheels with fatigue defects could also influence the track safety issues. This paper presents how different wheel defects can be monitored; together with a review of the most common wayside condition monitoring systems on the Swedish railway network. The study also describes how the decision making process could take advantage of the condition monitoring data in order to increase the achieved network capacity.

The measurement and improvement of track quality are key issues in determining both the restoration time and cost of railway maintenance. Applying the optimal tamping strategy helps reduce maintenance costs, making operations more cost effective and leading to increased safety and passenger comfort. In this paper, track geometry data from the iron ore line (Malmbanan) in northern Sweden, which handles both passenger and freight trains, are used to evaluate track geometry maintenance in cold climate. The paper describes Trafikverket’s (Swedish Transport Administration) tamping strategy and evaluates its effectiveness in measuring, reporting, and improving track quality. Finally, it evaluates the performance of the maintenance contractor and discusses the importance of the functional requirements stated in the outsourcing contracts.

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.