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. The long-term research goal is the development of a generic and transferrable rail corrugation index, which will help rail maintenance practitioners determine when rail corrugation is likely to generate unacceptable noise and vibration. Consequently, the research directly supports rail and vehicle asset management programs, helps reduce noise irritation for passengers and citizens in the vicinity of rail transit lines, and improves ride quality.