Rolling contact fatigue prediction using locomotive digital twin and a wheel-rail experimental program

Rolling contact fatigue (RCF) is the dominant degradation mode of wheels and rails in the railway sector. Furthermore, repairing activities on rails and wheels constitute the most expensive component of maintaining rolling stock and track infrastructure. RCF prediction is a complex task because it depends on several parameters that include vehicle speed, traction system characteristics, wheel-rail material properties, track layout, wheel-rail profiles, etc. Experimental approaches can only identify wheel-rail RCF after it has occurred, as wheel-rail forces and contact patch measuring technologies are still emerging and field experiments are only viable for a limited combination of vehicle-track operating parameters. This paper proposes a hybrid method that uses an experimental program to measure the actual wheel-rail material properties, field friction measurements and a numerical program performed through vehicle-track multi-body dynamic simulations, to produce more accurate predictions on RCF formation. First, tensile tests were performed on samples extracted from actual wheel-rail pieces. The dynamic friction-creep curve parameters were then measured at different track locations using a hand-held tribometer. The friction-creep parameters and material mechanical properties were used as inputs to the wheel-rail coupling of the AC locomotive multibody model. The latter also included a full traction control system model implemented through a co-simulation technique to produce a digital twin of an actual locomotive. A set of simulations were conducted on CQUniversity’s High Performance Computing Cluster. The results were postprocessed and presented as stress occurrence heatmaps overlayed on a material shakedown map which permits visualising the combination of operative parameters that promote RCF damage. The proposed method allows unprecedented insights on the root-cause of RCF formation and increased precision using the actual material mechanical properties and the locomotive digital twin. This innovative tool enables railway operators to predict wheel-rail health and maintenance regimes, reducing the overall cost of operating railways.