Weighing-in-motion wireless system for sustainable railway transport

Costa, Bruno J. Afonso; Martins, Rodrigo Lema Del Rio; Santos, Magno; Felgueiras, Carlos; Calçada, Rui

doi:10.1016/j.egypro.2017.10.260

Cited by 11 publications

(9 citation statements)

References 5 publications

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“…An answer to the search for new, more effective, cost-effective, and environmentally friendly modes of transport is intermodal transport (Abbassi et al, 2019). It largely relies on rail transport as a safer, more environmentally oriented, and potentially more effective alternative on long distances (Costa et al, 2017;Dedík et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Rail Transport Market in Poland

Marcysiak¹

2020

ERSJ

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of the Rail Transport Market in Poland

Marcysiak¹

2020

ERSJ

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“…The proposed methodology to estimate the wheel static load is successfully accomplished, as the load falls within the confidence interval. Although very widespread and efficient, WIM systems are more focused on identifying situations of vehicle weighing and overloading [ 16 , 17 , 18 , 19 ], and reported literature on systems capable of detecting situations of instability in railway circulation, such as unbalanced loads, has been limited so far.…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence

Silva

Guedes

Ribeiro

et al. 2023

Sensors

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The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs. This study aims to develop a methodology capable of automatically identifying unbalanced vertical loads considering the limits proposed by the reference guidelines. The research relies on a 3D numerical simulation of the train–track dynamic response to the presence of longitudinal and transverse scenarios of unbalanced vertical loads and resorting to a virtual wayside monitoring system. This methodology is based on measured data from accelerometers and strain gauges installed on the rail and involves the following steps: (i) feature extraction, (ii) features normalization based on a latent variable method, (iii) data fusion, and (iv) feature discrimination based on an outlier and a cluster analysis. Regarding feature extraction, the performance of ARX and PCA models is compared. The results prove that the methodology is able to accurately detect and classify longitudinal and transverse unbalanced loads with a reduced number of sensors.

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“…Onat et al approaches an interesting model based on the interpretation of wheel angular and vehicle translational velocities instead of measurements from the track or instrumented wheelsets [19]. Although most applications report the use of electrical strain gauges [20], including some recent work on the design of optimal strain gauges [21], some emerging technologies using piezoelectric sensors [22][23][24] and fiber optic sensors [25] are also available in the literature.…”

Section: Introductionmentioning

confidence: 99%

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

Pintão

Mosleh

Vale

et al. 2022

Sensors

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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.

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Weighing-in-motion wireless system for sustainable railway transport

Cited by 11 publications

References 5 publications

Analysis of the Rail Transport Market in Poland

Analysis of the Rail Transport Market in Poland

Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence

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

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