Hybrid proactive management of railway assets

Research Project, 2027 – 2028

The project will address the overall challenges in linking collected inspection and monitoring data to current status, future deterioration and operational safety levels of track and vehicles. An innovative feature of the project is that it takes a hybrid approach in several aspects:
• It combines data driven analyses with physical simulations where one important aim of the latter is to identified flawed data.
• Data are obtained from several sources such as from measurements, databases, and dedicated detectors. This allows e.g., investigations of correlation between different data sets.
• Data analysis is performed both using artificial intelligence / machine learning (AI/ML) and by ‘traditional’ statistical means. The aim here is to be able to both identify complex interaction patterns, and establish transparent descriptions of these relations.
With this approach, the aim is to expand the theoretical framework for predictive asset management in the railway sector.