Switch and crossing (S&C) faults are a major cause of track-related delays and account for a significant proportion of maintenance and renewal budgets for railway infrastructure managers around the world. Although various modelling approaches have been proposed in the literature for the simulation of vehicle–track dynamic interaction, wheel–rail contact and damage prediction, there is a lack of evaluation for combining these approaches to effectively predict the failure mechanism. An evaluation of S&C modelling approaches has therefore been performed in this article to justify their selection for the research interests of predicting the most dominant failure mechanisms of wear, rolling contact fatigue (RCF) and plastic deformation in S&C rails by recognising the factors that influence the accuracy and efficiency of the proposed modelling approaches. A detailed discussion of the important modelling aspects has been carried out by considering the effectiveness of each individual approach and the combination of different approaches, along with a suggestion of appropriate modelling approaches for predicting the dominant failure mechanisms.

Railway track switches experience high failure rates, which can be reduced by monitoring their structural health. The results obtained from a validated Finite Element (FE) model for train–track switch interaction have been introduced to support sensor selection and placement. For the FE models with nominal and damaged rail profiles, virtual strain sensor measurements have been obtained after converting the true strains to engineering strains. Comparisons for the strains before and after the introduction of the fault have demonstrated greater amplitude for the strains after fault introduction. The highest difference in strain amplitude is in the vertical direction, followed by the longitudinal and lateral directions.

A novel approach that utilizes numerical simulations of train-track interactions to determine the placement of sensors for the condition monitoring of railway switches has been demonstrated. The locations on the switches that would need to be monitored for the development of a specific RCF fault were obtained by utilizing the results from holistic MBS train-turnout interaction simulations in a damage prediction model. A detailed three-dimensional (3D) solid Finite Element (FE) model for replicating the similar track dynamic behavior and rolling contact interaction as the holistic MBS model was developed and validated. A 3D squat fault was introduced into the railhead of the FE model and simulations were run to help determine the placement of strain measurement sensors on the rail to detect its development. The results for the contact forces, Von-Mises stress and the engineering strains have been obtained from the simulations to help estimate the placement of sensors for the effective detection of the RCF squat fault.