Abstract

The three dimensional (3D) point cloud model and its 2D cross section profile of a 60kg/m rail were obtained by a 3D scanner. But, it’s inaccurate to calculate rail wear with the 2D cross section profile directly which may has a certain inclination along the vertical center line of the rail because the 2D profile was sampled from the random point cloud. The algorithms for aligning the extracted profile from the 3D point cloud model by rotating the field side rail web curve, and parallel shifting the same side rail base line was presented. The results show a good effect comparing the standard profile with the precision less than 0.1mm. Detail implementation of the algorithm was put forward by MATLAB and its feasibility and stability were verified. INSTRUCTION Wear is one of the major types of rail defects. With the increase of train speed, axle load and traffic density, the rail wear is more serious, which leads to the great rail profile changes, thereby affecting the performance of vehicle operation and dynamic forces between wheel and rail. Therefore, measuring the rail head profile, calculating the rail’s vertical and lateral wear accurately are important to the railway safety and economical operations (Telliskivi T, Olofsson U. (2004)). At present, there has been numerous research on measuring rail transverse profile and calculating rail wear, such as mechanical fixture contact measurement, laser non-contact measurement, and mechanical contact electrical measuring and so on. A Rail profile drawing device, P110B, has been developed by VOGEL&PLOTSCHER, and its precision can achieve 0.5mm. Die NextSense Mess-und Prufsysteme GmbH developed the equipment known as Calipri that records rail profile by laser technology, then calculates the rail wear automatically. MiniProf developed by Green Wood in Denmark is a portable rail profile measurement device which can measure the whole rail head profile. Data processing algorithms for rail wear calculation have been realized including radius

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