Predicting Railway Wheel Wear under Uncertainty of Wear Coefficient, using Universal Kriging

Railway wheel wear prediction is essential for reliability and optimal maintenance strategies of railway systems. Indeed, an accurate wear prediction can have both economic and safety implications. In this paper we propose a novel methodology, based on Archard's equation and a local contact model, to forecast the volume of material worn and the corresponding wheel remaining useful life (RUL). A universal kriging estimate of the wear coefficient is embedded in our method. Exploiting the dependence of wear coefficient measurements with similar contact pressure and sliding speed, we construct a continuous wear coefficient map that proves to be more informative than the ones currently available in the literature. Moreover, this approach leads to an uncertainty analysis on the wear coefficient. As a consequence, we are able to construct wear prediction intervals that provide reasonable guidelines in practice

Towards simulation-based optimisation of materials in railway crossings

Railway crossings are subjected to an intense load environment\ua0caused by the rail discontinuities needed to accommodate\ua0the passage of wheel flanges in intersecting traffic directions.\ua0This gives rise to high costs associated\ua0with repair and maintenance.\ua0For given traffic conditions, several approaches can be undertaken\ua0to mitigate the material degradation and hence reduce the life\ua0cycle cost.\ua0In the present thesis, the option of selecting a more suitable\ua0crossing material is explored.To obtain a guideline for material selection, the in-track\ua0performance of different materials during the life of a\ua0crossing needs to be predicted.\ua0In this work, an existing simulation methodology is extended to\ua0improve robustness and computational efficiency.\ua0The methodology is able to account for the\ua0dynamic vehicle-track interaction, resolve the\ua0elasto-plastic wheel-rail contact, and account for the main\ua0damage mechanisms related to the running surface of a crossing.In this thesis, the methodology is updated with a metamodel\ua0for plastic wheel-rail normal contact that is introduced to meet\ua0the computational challenge of a large number of finite\ua0element simulations.\ua0The metamodel is inspired by the contact theory of Hertz, and\ua0for a given material it computes the size of the contact patch and the maximum contact pressure as a function of the normal force and the local curvatures of the bodies in contact. The model is calibrated based on finite element simulations with an elasto-plastic material model.\ua0It is shown that the metamodel can yield accurate results\ua0while accounting for the inelastic material behaviour.Furthermore, the simulation methodology is\ua0employed to compare the performance of two rail steel\ua0grades that are used in crossings: the fine-pearlitic steel\ua0R350HT and the austenitic rolled manganese steel Mn13.\ua0A representative load sequence generated by means of Latin hypercube\ua0sampling, taking into account variations in worn wheel profile, vehicle speed and\ua0wheel-rail friction coefficient, is considered.\ua0After 0.8 MGT of traffic, it is predicted that the use of rolled Mn13\ua0will result in approximately two times larger ratchetting strain as\ua0compared to the R350HT

Reliability assessment of freight wagon passing through railway turnouts using adaptive Kriging surrogate model

Railway turnout (RT) is a crucial component of railway infrastructure that consists of several components. Assessing the derailment probability of freight wagons passing through the turnout is crucial for quantifying failure risks and optimizing the performance of the freight wagon-turnout system (FWTS). However, existing assessment methods often require extensive model evaluations and impose substantial computational costs. To address this issue, an efficient reliability analysis method is established for assessing the derailment risk at RTs. Firstly, a dynamic model is developed to capture the wheel-rail dynamic interaction and the numerical model is validated by field tests. Secondly, to reduce the computational cost in the reliability analysis, an efficient adaptive Kriging method based on an error stopping criteria and a learning function is adopted to estimate the failure probabilities under multiple failure modes of wheel derailments. Based on the efficient learning function and convergence criterion, accurate failure probability results can be obtained with a small number of multibody and finite element coupled dynamic simulations. Furthermore, the prediction accuracy of the proposed method in capturing random characteristics for FWTS is evaluated. Finally, the influence of the evolution of rail wear on the failure probability is further discussed

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Simulation of wheel and rail profile wear: a review of numerical models

The development of numerical models able to compute the wheel and rail profile wear is essential to improve the scheduling of maintenance operations required to restore the original profile shapes. This work surveys the main numerical models in the literature for the evaluation of the uniform wear of wheel and rail profiles. The standard structure of these tools includes a multibody simulation of the wheel-track coupled dynamics and a wear module implementing an experimental wear law. Therefore, the models are classified according to the strategy adopted for the worn profile update, ranging from models performing a single computation to models based on an online communication between the dynamic and wear modules. Nevertheless, the most common strategy nowadays relies on an iteration of dynamic simulations in which the profiles are left unchanged, with co-simulation techniques often adopted to increase the computational performances. Work is still needed to improve the accuracy of the current models. New experimental campaigns should be carried out to obtain refined wear coefficients and models, while strategies for the evaluation of both longitudinal and transversal wear, also considering the effects of tread braking, should be implemented to obtain accurate damage models