Quantifying the relationships between rail profile quality indices and inservice wheel-rail contact conditions

In order to maintain safe and efficient operations, railway systems periodically use large, special purpose, trackbound grinding machines to re-establish the desired profiles of rails, and to remove surface damage that has occurred under operating conditions. The accuracy of the grinding process is typically measured using quality indices to compare the resulting rail profile to its target shape.

An Integrated Software Suite for Rail Condition Analysis using Machine Learning

Rail transit and freight rail properties apply rail grinding to maintain rail condition and ensure satisfactory performance of rail infrastructure systems. The proposed research investigates and applies a variety of computationally intelligent algorithms to establish useful relationships between rail corrugation, noise generation, and vibration. These relationships will support more timely and effective rail grinding interventions. The algorithms will process real-world rail corrugation, noise, and vibration data collected from three rail transit properties in North America.

Applying Machine Learning to Develop Meaningful Rail Condition Indices

Rail transit and freight rail properties apply rail grinding to maintain rail condition and ensure satisfactory performance of rail infrastructure systems. The proposed research investigates and applies a variety of computationally intelligent algorithms to establish useful relationships between rail corrugation, noise generation, and vibration. These relationships will support more timely and effective rail grinding interventions. The algorithms will process real-world rail corrugation, noise, and vibration data collected from three rail transit properties in North America.