This document proposes a methodology for enhancing the scheduling of maintenance activities for railway infrastructure using deterioration and optimisation models. The approach combines a deterioration model with a semi-Markov model through the degradation variable (Weibull distribution). The objective is to determine the optimal risk level that triggers maintenance interventions to achieve the most significant economic benefits. The creation of digital twins for both assets and infrastructures facilitates the use of real-time operational data and enables the implementation of analytical models to support maintenance decision-making. Organisations identify and collect relevant asset data, convert this data into model inputs and subsequently use the model outputs to evaluate and select the most advantageous maintenance strategies. This methodology advances current railway decision-support systems for preventive maintenance, transitioning from a traditional table-based approach (subjectively defined risk levels) to an objective method that identifies the risk level which triggers the maintenance intervention based on economic factors. The model can be applied to a wide range of railway assets and is designed to be integrated into a digital twin system. A case study demonstrates its application to the management of track sections. The paper highlights the critical role of data-driven approaches in achieving economically efficient maintenance strategies, while also acknowledging that other key factors in the railway sector, such as safety and transport efficiency, complement these benefits.
Carballo-Menayo et al. (Sun,) studied this question.