A hybrid predictive methodology for head checks in railway infrastructure

Abstract

This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis.