The study presented in this paper proposes a method to estimate the Remaining Useful Life (RUL) of railway tracks determined by wear and taking into account various track geometry and usage profile parameters. The relation between these parameters and rail wear is established by means of meta-models derived from physical models. These models are obtained with regression analysis where the best fit is found from a relatively large set of numerical experiments for various scenarios. The specific parameter settings for these scenarios are obtained by using the Latin Hypercube Sampling (LHS) method. Furthermore, for the rail profile, which is one of the input parameters for the meta-model, it is shown that the evolution due to wear in moderate curves can be characterized by only one parameter. The findings in this work including are valuable for Infrastructure Managers (IMs) and can easily be implemented in maintenance decision support tools.

While the development of prognostic models is nowadays rather feasible, the implementation and validation thereof can still create many challenges. One of the main challenges is the lack of high-quality input data like operational data, environmental data, maintenance data and the limited amount of degradation or failure data. The uncertainty in the output of the prognostic model needs to be quantified before it can be utilised for either model validation or actual maintenance decision support. This study, therefore, proposes a generic framework for prognostic model validation with limited data based on uncertainty propagation. This is realised by using sensitivity indices, correlation coefficients, Monte Carlo simulations and analytical approaches. For demonstration purposes, a rail wear prognostic model is used. The demonstration concludes that by following the generic framework, the prognostic model can be validated, and as a result, realistic maintenance advice can be given to rail infrastructure managers, even when limited data is available.

x verkanting, asbelasting, boog radius, materiaalhardheid, wrijvingscoëfficiënt en de primaire longitudinale en laterale stijfheid van het treinstel relevant zijn. Na het verkrijgen van inzicht in de meest dominante parameters worden de metamodellen ontwikkeld die de relatie tussen spoorstaafslijtage en het gebruiksprofiel van spoorwegen bepalen. De geometrie van deze spoorwegen dient ook als invoerparameter van de metamodellen. De fit van deze modellen is gebaseerd op een grote dataset gegenereerd uit verschillende scenario's. De selectie van deze scenario's wordt uitgevoerd door middel van een Design of Experiments (DOE) methode, bekend als de Latin Hypercube Sampling (LHS). Deze methode genereert willekeurig scenario's met verschillende parameterinstellingen. De beste fit van het metamodel wordt vervolgens gevonden met behulp van de Response Surface Methodology. De output van de metamodellen voor zowel slijtage als RCF wordt vervolgens gevalideerd met veldmetingen. Wervelstroommetingen werden gebruikt om de prestaties van de RCF-metamodellen te valideren. De validatieresultaten waren positief, maar de enige tekortkoming van het model was dat het de grootte van een scheur niet kon voorspellen. Om deze tekortkoming op te heffen, zijn daarom data-aangedreven methoden geïntroduceerd en een hybride methode ontwikkeld. De validatie van de slijtage metamodellen werd uitgevoerd door middel van gemeten spoorstaafprofielen met behulp van draagbare meetapparatuur zoals Railmonitor en MiniProf. Bovendien werd vanwege de grote onzekerheid in de invoerparameters een stochastische aanpak gekozen om infrastructuurbeheerders de voorspelde spoorslijtage met zekere vertrouwensgrenzen te bieden. Het voorspelde gemiddelde en de variatie op het metamodel resultaat, in dit geval het spoorstaafslijtage oppervlak, kwam overeen met de resultaten van de veldmetingen.

Rail wear management based on accurate rail wear prediction is essential for railway maintenance. The implementation of rail wear prediction models in maintenance decision tools is not yet available due to detailed modelling and the absence of direct coupling with operational conditions. A method that does not provide any confidence interval on the prediction is not very helpful if one wants to use the results of the prediction for maintenance decision-making and there is variation in the input. Therefore, in this study a wear prediction model that does take into account these limitations is used to predict the amount of rail wear with certain confidence bounds. The uncertainty in the output of the model is quantified. This is realized by considering probability distribution functions for the input parameters and analytical analyses. The results obtained from these analyses are then compared with field measurements and a good agreement is found.

This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis.