An Artificial Intelligence Approach for Automated Asset Management of Railway Systems

Automated diagnostic and predictive asset manage-ment capabilities are of paramount importance in the era of connected and automated cooperative mobility. A diagnostic vehicle can scan the rail network and process sensor measurements to prevent incoming disruptions and ensure smooth operation of automated transportation services. This requires the development of reliable algorithms that enable early warning and predictive asset management. An algorithm based on artificial intelligence techniques is presented here. The algorithm analyses diagnostic measures and relates them to observed faults on the rail network. In operation mode, the algorithm predicts maintenance needs based on current measurements.