A method for eliminating velocity effects in railway dynamic measurement datasets

Abstract

This study presents a new method to mitigate the effects of train speed on dynamic measurement data acquired by high-speed diagnostic trains for railway track geometry evaluation. Variations in train velocity are shown to significantly distort signal characteristics, such as wavelength and amplitude, which can hinder accurate defect detection. To overcome this limitation, a speed-independent data processing technique is developed that transforms acceleration signals into displacement in the spatial domain, effectively normalising the data across different speeds. The proposed approach is validated using vertical acceleration data collected from axle box-mounted sensors on a Turkish State Railways (TCDD) high-speed diagnostic train. Experimental results confirm that the method significantly reduces speed-induced discrepancies, enhancing the consistency and reliability of track condition assessments. The study further demonstrates that consistent sensor placement, vehicle orientation, and measurement direction are critical to ensuring data comparability. The findings contribute to more accurate infrastructure monitoring and support improved maintenance planning in high-speed rail networks.