Integrated Computational Materials, Process and Product Engineering – IC-MPPE 2022

With current development in sensor technologies, data processing, numerical simulations, and life prediction methodologies, there are increasing possibilities for monitoring and predicting track health. This has led to an increased interest in so-called “digital twins”, i.e. digital models of the actual railway track. Ideally, a “digital twin” should be updated as the track characteristics evolve, and also be able to predict future development. The current presentation will set out from the concept of a “digital twin” and focus on some challenges related to the concept. This includes how the current track status can be quantified and which parameters that should be employed in a track health quantification. Such a quantification requires operational parameters. However, measurable parameters are often not parameters that can be directly included in predictive models for track health. This calls for a translation that is sometimes far from obvious. An example of this is the translation between (fatigue) damage, which has to be translated to measurable physical deterioration (e.g., in the form of fatigue crack length). Finally, a full track health assessment will include a multitude of trac components and pertinent deterioration phenomena. To limit the scope, the presentation will highlight some of these and exemplify how they can be (and are) addressed.
The work is partly funded by the European Union’s Horizon 2020 research and innovation programme in the Shift2Rail projects In2Track3 under grant agreement No.101012456.