In-field railhead crack detection using digital image correlation

Abstract

A significant challenge for railway infrastructure managers is to know when and how to maintain rails. Often, the critical missing information is the current health status, in particular information about rail cracks. Therefore, a new method for rail crack detection is proposed. By utilizing a train-mounted camera system, a single measurement train can monitor a large rail network. The system uses Digital Image Correlation (DIC) to measure the strain fields due to rail bending caused by the measurement train. Promising results are obtained under laboratory conditions. The identified cracks are correlated to the actual crack network, characterized by serial-sectioning microscopy. Furthermore, finite element simulations show the method's high sensitivity to crack depths. Knowing the crack depths enable infrastructure managers to optimize the rail maintenance.