Acoustic Roughness Measurement of Railway Tracks: Running Surface Detection and Compensation of Lateral Movements for Optical Measurements on a Train

Abstract

Rolling noise is a significant contributor to railway noise. Wheel and rail roughness are decisive for the emitted noise level. An optical measurement method installed on a moving train is suitable for closer monitoring of the rail surface condition. A measurement setup based on the chord method requires the sensors to be positioned in a straight line along the direction of measurement and in a stable lateral position. Measurements should always be performed within the shiny and uncorroded running surface, even when there are lateral movements of the train. In this study, concepts for the detection of the running surface and the compensation of lateral movements are investigated in a laboratory setting. The setup consists of a vertical lathe with a ring-shaped workpiece that incorporates an implemented artificial running surface. The detection of the running surface based on laser triangulation sensors and a laser profilometer is investigated. It is shown that the running surface can be detected using a laser profilometer that measures the intensity of the reflected laser light. It is possible to detect the lateral position and the width of the running surface. A linear positioning system is proposed to adjust the lateral position of the sensors based on the running surface detection of the laser profilometer. When the lateral position of the measuring sensor is disturbed by a movement with a wavelength of 18.85 m, the linear positioning system can keep the laser triangulation sensor inside the running surface for 98.44% of the measured data points at a velocity of approximately 7.5 km h−1. The mean positioning error is 1.40 mm. By implementing the proposed system on the train, future studies can be conducted to examine the lateral position of the running surface as a function of the various operational parameters of the train.

Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning

“…In laboratory tests with an artificial running surface carried out by Mauz et. al [ 17 ], a clear distinction could be made between the running surface and the corroded edge areas based on the intensity of the light reflected from the surface. The detected intensities of the three different rail surfaces from field experiments are analyzed.…”

“…The element size of × 50 ( × ) is an empirical compromise between the filter effect of the surface elements and the level of detail required to determine the running surface. The selection of features is based on the findings of the measurements carried out by Mauz et al [ 17 ] and the typical lateral position of the running surface. The following features are obtained based on the findings.…”

“…Ye et al [ 16 ] presented a methodology for the detection of rail surface defects. Mauz et al [ 17 ] showed that an artificial running surface in a laboratory environment could be detected using a laser profilometer. The intensity of the light reflected from the surface was determined to distinguish between different surface conditions.…”