Weighing in motion and wheel defect detection of rolling stock

Meixedo, Andreia; Gonçalves, Ana Clara Campagnolo Real; Calçada, Rui; Gabriel, Joaquim; Fonseca, Hélder; Martins, Rodrigo Lema Del Rio

doi:10.1109/expat.2015.7463220

Cited by 8 publications

(8 citation statements)

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“…The rail load (measured by several force sensors) is a main diagnostic parameter where limit values are monitored. A similar solution based on load measurement was used in the article (Meixedo et al, 2015). The Grupo Revenga from Spain is the system producer which is also a world's provider of different railway inspection systems.…”

Section: Wheel-flat Detection Methods Algorithms and Systemsmentioning

confidence: 99%

Advanced acoustic signal analysis used for wheel-flat detection

Komorski

Szymański

Nowakowski

et al. 2021

Lat. Am. j. solids struct.

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One of the most important aspects in the operation of rail vehicles is the implementation of transport while ensuring safety and comfort of passengers. Technical condition of the wheel rolling surfaces has a direct impact on these factors. These elements are subject to wear and tear in a continuous and discrete form. One of the discrete wear forms is the wheel-flat on wheel rolling surfaces that generate impulse noise. This translates into a significant deterioration of vibroacoustic comfort, and in extreme cases also to a greater risk associated with e.g. derailment of the vehicle. Therefore, it is desirable in particular by the rolling stock operator to carry out cyclic diagnostics and monitoring of the condition of wheel rolling surfaces. This paper is a continuation in a series of research articles carried out by the authors related to vibroacoustic diagnostics of wheel rolling surfaces in light rail vehicles. As part of this article, acoustic measurements were carried out at a dedicated track-side system during so-called pass-by tests. Acoustic signals were analyzed in accordance with the Fourier and Hilbert transforms. Additionally, the main assumptions of vibroacoustic diagnostics and analyses of point measures were used. This allowed for the development of yet another way of monitoring the occurrence of the problem of wheel flats in rail vehicles.

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Section: Wheel-flat Detection Methods Algorithms and Systemsmentioning

confidence: 99%

Advanced acoustic signal analysis used for wheel-flat detection

Komorski

Szymański

Nowakowski

et al. 2021

Lat. Am. j. solids struct.

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“…The steel box was divided into four clusters, the concrete slab was divided into three, and each arch was also divided into three clusters, with the temperature being introduced in the upper and bottom part of the arch section. Using the measurements of the optical sensor network installed at both ends of the bridge, the train speed and axle configuration were obtained, and the train type was identified [24]. The dynamic analyses mentioned hereinafter were carried out for two of the passenger trains that normally cross the Sado Bridge, namely the Alfa Pendular (AP) train and the Intercity (IC) train.…”

Section: Dynamic Numerical Simulationmentioning

confidence: 99%

Online unsupervised detection of structural changes using train–induced dynamic responses

Meixedo

Santos

Ribeiro

et al. 2022

Mechanical Systems and Signal Processing

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“…In contrast, wayside measurement systems are currently a preferable solution to detect wheel flats, as the condition of all wheels evaluates during the passage of trains [21][22][23][24][25][26][27][28]. Liu et al [29] proposed a quantitative decision approach based on wheel impact data that illustrates the actual condition of the wheels.…”

Section: Introductionmentioning

confidence: 99%

“…In previous studies, for the successful detection of wheel defects, sensors have been installed along an equivalent wheel perimeter length [21,36]. However, the study performed by Mosleh et al [37] shows that this amount of sensors is not necessary to identify the defective wheel.…”

Section: Introductionmentioning

confidence: 99%

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Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis

et al. 2021

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The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon.

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Weighing in motion and wheel defect detection of rolling stock

Cited by 8 publications

References 0 publications

Advanced acoustic signal analysis used for wheel-flat detection

Advanced acoustic signal analysis used for wheel-flat detection

Online unsupervised detection of structural changes using train–induced dynamic responses

Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis

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