Development and Validation of a Weigh-in-Motion Methodology for Railway Tracks

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 5; p. 1976
• Main Authors: Pintão, Bruno, Mosleh, Araliya, Vale, Cecilia, Montenegro, Pedro, Costa, Pedro
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.03.2022; MDPI
• Subjects: Confidence intervals; Derailments; estimation of static load; Load; Mechanical properties; Monitoring systems; Motion; Motion systems; Railroad ballast; Railroads; Railway tracks; railway vehicle; Sensors; Shafts (machine elements); Static loads; Strain gauges; Traffic speed; Trains; Vehicles; Vertical forces; Wagons; wayside condition monitoring; weigh-in-motion system; Weighing in motion; estimation of static load; weigh-in-motion system; railway vehicle; strain gauges; wayside condition monitoring
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22051976

In railways, weigh-in-motion (WIM) systems are composed of a series of sensors designed to capture and record the dynamic vertical forces applied by the passing train over the rail. From these forces, with specific algorithms, it is possible to estimate axle weights, wagon weights, the total train weight, vehicle speed, etc. Infrastructure managers have a particular interest in identifying these parameters for comparing real weights with permissible limits to warn when the train is overloaded. WIM is also particularly important for controlling non-uniform axle loads since it may damage the infrastructure and increase the risk of derailment. Hence, the real-time assessment of the axle loads of railway vehicles is of great interest for the protection of railways, planning track maintenance actions and for safety during the train operation. Although weigh-in-motion systems are used for the purpose of assessing the static loads enforced by the train onto the infrastructure, the present study proposes a new approach to deal with the issue. In this paper, a WIM algorithm developed for ballasted tracks is proposed and validated with synthetic data from trains that run in the Portuguese railway network. The proposed methodology to estimate the wheel static load is successfully accomplished, as the load falls within the confidence interval. This study constitutes a step forward in the development of WIM systems capable of estimating the weight of the train in motion. From the results, the algorithm is validated, demonstrating its potential for real-world application.