An Accurate and Fast Method to Inspect Rail Wear Based on Revised Global Registration

Abstract

Rail wear inspection is vitally important in the railway industry. Conventional methods mainly use manual or static measurements to detect rail wear, which are inefficient, imprecise, and unreliable. To improve the accuracy and efficiency of rail wear inspection, a dynamic detection system based on a revised fast global registration (RFGR) algorithm was employed. First, the framework for online detection of rail wear with multi-profile was put forward to reduce the influence of vibrations of individual sections. Second, the RFGR method was proposed by using a robust weight function to convert the non-convex registration model to a convex problem, and the Levenberg–Marquardt method was used to solve nonlinear least-squares systems robustly. Finally, the Hausdorff distance was introduced to visualize the distance between the wear profile and the reference profile after alignment. The experimental results demonstrated that the RFGR algorithm was more accurate, robust, and effective than iterative closest point (ICP), sparse ICP, Yi’s sparse ICP, and the fast global registration algorithm. For actual wear detection, the proposed method was more efficient and robust for the online dynamic detection of rail wear when compared with the single-profile-section-based inspection method.