Abstract

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.

Highlights

  • Train WIM is of great interest in controlling axles and vehicle loads, detecting wheel defects and predicting train derailment in order to guarantee acceptable train operations and safety [1–5]

  • WIM systems are essential for freight wagons with overloaded or unbalanced loads, as it leads to damages to the infrastructure on the one hand and enhances the risk of derailment on the other hand

  • The proposal considered in this article is a three-phase methodology to evaluate train static loads in WIM from a wayside monitoring system

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Summary

Introduction

Train WIM is of great interest in controlling axles and vehicle loads, detecting wheel defects and predicting train derailment in order to guarantee acceptable train operations and safety [1–5]. Sensors are installed directly on the wheels, axle or bogies of the rail vehicle for measuring the dynamic loads. These types of devices are defined as onboard monitoring systems. Bernal et al [9] reviewed recent onboard condition monitoring sensors, systems, methods and techniques, aiming to define the present state of the art and its potential application for freight wagons By using this approach, Kanehara and Fujioka [10] estimate vehicle axle loads using electrical strain gauges. The second approach uses measurements from wayside monitoring systems, which assess the train loads from the dynamic track response This indirect method can be categorised by: (1) WIM on the track; (2) bridge weigh-in-motion, (B-WIM) [14–17]. The validation of the proposed WIM methodology considering different types of trains, is a clear step forward in terms of the effectiveness of the proposed methodology, which allowed for a complete implementation for real-world application

Layout Scheme of the WIM System
Description of the Train–Track Coupling Model
Train Model (UIC-60)
The models arethe based the work by Zhai
Dynamic
Unevenness Profile
Methodology
Sensitivity Analysis Regarding the Axle Dynamic Loads
Validation of the Proposed Methodology
Conclusions

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