Data-driven predictive maintenance of rails

Have you ever arrived late with the train? Or did a small bump spill some of your coffee in the coupe? All that and much more are caused by rail defects posing a serious concern for railway infrastructure managers. Initiated rail cracks are not necessarily easily detectable nor predictable; they are expensive to repair and expensive not to repair, and dangerous to ignore. Performing maintenance operations before defect detection is emerging as a transformative approach with the potential of making the railway safer and more cost efficient. With this industrial PhD project, we develop data-informed mathematical computer models that can help realizing predictive maintenance in the railway business.