As Europe’s railways expand, their safety and efficiency depend increasingly on knowing every train’s position....
Satellite positioning set to enhance train localisation
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Predictive Maintenance for Train Traction Systems: Quantitative Modelling and Analysis
Description: This deliverable details the mathematical models developed to estimate the economic benefit of Predictive Maintenance (PdM), for different business scenarios. It is part of RECET4Rail’s work on Big Data and Artificial Intelligence applied to the smart and predictive maintenance of the traction system. PdM exploits the Prognostics and Health Management (PHM) capability, the latter making use of knowledge, information, and data to detect anomalies, diagnose, and prognose. The goal of doing so is to improve maintenance scheduling, thus yielding larger system availability at smaller maintenance costs. A clear understanding of the actual business opportunities of PdM for traction systems is fundamental for its full development. This valuable knowledge provides the opportunity for a just-in-time and just-right maintenance strategy, which will in turn lead to maximising system reliability and minimising maintenance costs, as well as losses.
Target audience: Rail Transport Service Providers
How it brings us closer to achieving better rail for Europe: This deliverable contributes to better rail for Europe through the model that it proposes, which is meant to support decisions on multiple levels, ranging from operational and tactical to broader, strategic ones. In general, the components of the system wear out at different rates, and, consequently, maintaining all components at the same time is not cost-efficient in ensuring requisite reliability. More precisely, every time the system undergoes a stop, there is an opportunity to also maintain the components with the shorter residual life, which has an impact on overall system reliability. For this reason, the research performed under this deliverable is significantly beneficial for the availability and service continuity of trains. This particular output should be understood against the backdrop of RECET4Rail’s overall contributions to the advancement of work in domains such as digitalisation applied to traction, environmental sustainability (especially devising carbon-free traction systems), and/or reinforcement of standardisation to lower complexity and costs.
More information on this topic: RESET4RAIL
System Validation Report: Life Cycle Engineering for Urban Railway Bridges
Description: A tool for Life Cycle Engineering prognoses for bridges was developed within IN2SMART and provides a common approach to perform short, mid, and long-term forecasts (up to 60 years) for maintenance and replacement measures. This deliverable focuses on the validation report of the previously developed tool for urban railway bridges, using the three main approaches of requirement fulfillment, KPI validation, and software validation. It aims to develop a decision support system at operational, tactical, and strategic levels for civil assets. The associated tactical demonstrator is intended to support the identification of the most appropriate action for track geometry and to enable the formulation of prioritised plans. The package has further enabled the exploration of crucial drivers around network access. It has also analysed and evaluated different maintenance strategies, with particular attention to preventive maintenance. The work carried out in the IN2SMART2 project includes the assessment of the extent to which the described demonstrator satisfies system requirements, particularly validation against user needs.
Target audience: Rail Transport Service Providers
How it brings us closer to achieving better rail for Europe: The Life Cycle Engineering for Bridges comprised in this deliverable responds to the current demand for a step change in asset management to be delivered through innovative technologies and new economic possibilities. Its added value in achieving better rail for Europe is that it enables its users to first compare different maintenance strategies and then evaluate demanded budgets for a technical optimum solution. This achievement is closely linked to the Intelligent Asset Maintenance Pillar included in the Multi-Annual Action Plan of EU-Rail’s predecessor, Shift2Rail. Therefore, the deliverable contributes to the broader aim of accelerating the creation of new and optimised strategies, frameworks, processes, methodologies, tools, products, and systems, intended for the implementation of risk-based, prescriptive, and holistic asset management in the rail sector.
More information on this topic: IN2SMART2
System Validation Report: On-Track Machine Decision Support Tool
Description: A significant amount of track geometry data and associated maintenance records have been investigated for algorithm development in the project IN2SMART2. Utilising frequent track geometry measurements, models have been developed to predict the deterioration of track geometry and recommend when an intervention is required and the most appropriate type of intervention, based on the effectiveness of On-Track Machines. The work performed within the scope of this deliverable has used the outputs from the developed algorithms and models to show where the demonstrator is providing effective decision support. The deliverable describes the testing and validation of the algorithms and models as implemented in the On-Track Machines Decision Support Tool demonstrator. A single route and the full Great Britain rail network have been assessed to validate the On-Track Machines Decision Support Tool, in terms of the accuracy of the predicted plain-line track degradation and volume of On-Track Machines activities.
Target audience: Rail Transport Service Providers
How it brings us closer to achieving better rail for Europe: This deliverable addresses the demand for a step change in asset management to be delivered through innovative technologies and new economic possibilities in the rail sector. The assessment of KPIs performed under the scope of this deliverable shows that the On-Track Machines Decision Support Tool has the potential to provide cost savings in several key areas. Among them is maintenance planning and delivery, since the tool automates some of the manual activities pertaining to the planning of On-Track Machines’ maintenance. The tool also has the potential to improve asset life, by selecting the most appropriate intervention date and type, so as to defer renewal. Finally, it is foreseen to boost train performance while decreasing safety risk. This is because the tool helps reduce the proportion of tracks in the “poor” or “worse” track Quality Band. Work has also been conducted to assess how environmental data, specifically weather data, could be included in the tool to further enhance the prediction models. The deliverable supports Shift2Rail’s Intelligent Asset Maintenance Pillar, a driver in delivering innovative asset management and an accelerator in creating new and optimised strategies, frameworks, processes and methodologies, tools, products, and systems, intended for the implementation of risk-based, prescriptive, and holistic asset management in the rail sector.
More information on this topic: IN2SMART2
Rail Fastener Anomaly Detection Demonstrator
Description: This deliverable comprises the actual Rail Fastener Anomaly system demonstrator developed under this project and demonstrated at the IN2SMART2 final event in Rome in November 2022. The deliverable builds upon previous work done within IN2SMART, incorporating the rail fastener sensor referred to as the Lindometer. The deployment of the Lindometer was driven by deficiencies in existing rail fastener anomaly detection systems when faced with the harsh environment encountered in the north of Sweden. The demonstrator for a prototype rail fastener anomaly detection system was created according to KPIs appropriate for the maturity level of the demonstrator. Performance aspects of benefit to the stakeholders of the demonstrator were also closely considered. The demonstrator uses a state-of-the-art eddy-current sensor mounted on the vehicle. Sensors were optimised to measure the surface of any conductive material below the sensor. The measurement was analysed by computing an algorithm developed to extract any anomaly for the rail fastener systems. The R&I work has also been aimed at constructing a decision support data access system, by means of a cloud-based front end for operators to access appropriate decision support data for maintenance planning on the rail fasteners systems.
Target audience: Rail Transport Service Providers
How it brings us closer to achieving better rail for Europe: In line with the demand for a step change in asset management, this deliverable brings a positive impact in achieving better rail because it enables automatic inspection with consistency of results, irrespective of climate. As such, it contributes to increasing the effectiveness of rail fastener maintenance while lowering associated costs. In doing so, the deliverable manages to capitalise on Shift2Rail’s Intelligent Asset Maintenance Pillar, a driver to deliver innovative asset management and an accelerator creating new and optimised strategies, frameworks, processes and methodologies, tools, products, and systems for the implementation of risk-based, prescriptive and holistic asset management in the rail sector. sector.
More information on this topic: IN2SMART2