Digital Twin for condition based maintenance within a railway infrastructure testing lab

Abstract

This article presents a digital twin development in a railway case, in order to improve operations and maintenance decisions, aligned with an asset management strategy. A digital framework for the sustainable management of these assets is defined with the purpose of facilitating the implementation on a cloud platform, searching the generation and sharing of the produced models and the evaluation from different perspectives. The developed digital twin allows for the digital representation of railway lines and vehicles, the connection between different entities based on an ontology, the management of data ingestion and storage, and the administration of models for the detection, diagnosis, and prognosis, as well as the representation and control of the level of risk of the assets. When emulation of railway line degradation is searched, different types of data are combined, from on-board sensors in railway vehicles, and physical behaviours, up to machine learning algorithms for estimation. In this way, the degradation behaviour model for the railway line is shown and validated through intelligent models easily replicated in several areas of the railway network, showing risk levels for each one.