Funded by: STW, ProRail, NS/NedTrain
Duration: May 2017 until May 2021
Can one improve the reliability of the (Dutch) railroads, and reduce the number of disruptions?
We think we can, by deploying advanced data analytic techniques. Key idea is to develop novel techniques to learn the failure behavior of railroad elements with machine learning techniques, and get more information about the causing factors. Using this information, we can repair or replace a railroad element just before it fails, thereby reducing the railroad’s planned and unplanned downtime.
Since the success of big data analytics crucially relies on an effective combination with domain knowledge, we integrate machine learning with existing and novel algorithms for fault tree analysis, a prominent technique in reliability engineering to represent the propagation of failure
through a system.
We will closely collaborate with ProRail, the Dutch railroad asset manager, and NS, the rolling stock maintenance company, and analyze four urgent systems: grinding of rail, the SprinterLightTrain, train-wheel contact, and train-infra systems.