Dynamic rail wear measurement: integration of RTK GNSS, IMU, and laser

Abstract

Currently, laser sensors are widely utilized in railway systems for monitoring rail wear. However, the lack or insufficiency of rail waist data poses a challenge to accurately match profiles and calculate wear. In this paper, we propose a novel approach for dynamic rail wear monitoring that comprises three major modules: a filtering methodology to smooth rail profile data, fine compensation to calibrate the angle distortion, and fast profile alignment. Initially, the AF technique and Kalman filter are applied to reconstruct the profile. This is followed by integrating real-time kinematic global navigation satellite system, inertial measurement unit, and laser to precisely correct the rail profile dynamic distortion. Additionally, genetic algorithms-least squares method is used for rapid determination of the R20mm center of the profile. Rigorous testing and trials have demonstrated that our proposed approach achieves precision as low as 0.03 mm with significant potential in dynamic rail wear monitoring.