Detecting corrugation defects in harbour railway networks using axle-box acceleration data

Heusel, Judith; Baasch, Benjamin; Riedler, W; Roth, Michael; Shankar, Sangeetha; Groos, Jörn Christoffer

doi:10.1784/insi.2022.64.7.404

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…Another undesirable condition affecting the surface geometry of the rails is corrugation. This undulatory defect consists of short-length waves distributed along the top of the rail that may cause strong forces at the wheel-rail contact, as well as high noise levels [52]. Xie et al [53] proposed a machine-learning (ML) methodology to identify and characterize rail corrugation in metro lines.…”

Section: Rail Condition-based Assessmentmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

et al. 2023

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Railways are one of the most important means of transportation, allowing people and goods to move quickly, environmentally beneficially, and efficiently over long distances. To ensure safe and reliable operations, regular condition-based assessments of trains and track are of paramount importance. Drive-by methodologies, which utilize data collected by onboard monitoring systems as the vehicle travels over the track, have gained popularity as an economically viable strategy for monitoring extensive track networks as well as vehicles traveling on them. This paper presents a critical review of these methodologies applied to railway tracks and vehicles. It assesses research on track irregularities, rail conditions, and the condition of rail supporting elements, highlighting important early developments and recent papers that provide insights into future practical applications. Additionally, the paper explores works related to global vehicle condition evaluation, focusing on the identification of suspension and wheelset element damage and also discussing challenges towards commercial application. The findings suggest that drive-by methodologies have several promising future applications. These include track maintenance optimization, proactive fault detection, predictive maintenance, track performance evaluation, vehicle health monitoring, and data-driven decision-making. By leveraging drive-by assessments, railway operators can optimize maintenance efforts, detect faults early, predict remaining component life, evaluate track performance, monitor vehicle health, and make informed decisions based on data analysis. Finally, a comprehensive conclusion summarizes the achievements thus far and provides perspectives for forthcoming developments. The future practical applications of drive-by methodologies discussed in this review have the potential to revolutionize railway track and vehicle assessments, leading to safer and more efficient railway operations in the days to come.

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Section: Rail Condition-based Assessmentmentioning

confidence: 99%

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

et al. 2023

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“…In particular, the use of acceleration sensors on axle-box has become more popular for detecting track irregularities of different wavelengths and occurrence. Concretely, different applications include wheel flat [171], crossings monitoring [174,175], rail corrugation [176,185]; [188]), roughness derivation [178,179], rail joints [155], settlement and dipped joint ( [189]) and other railway elements [149,190]. In the aim of predictive maintenance, first works focused on visual description of the patterns that elements and defects produce on time-frequency domain [149, 168, 169, 177-179, 182-184, 190].…”

Section: Railway Track Surveyingmentioning

confidence: 99%

Learning from limited labelled data: contributions to weak, few-shot, and unsupervised learning

Rodríguez¹

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La presente tesis es el resultado de un largo camino formativo en el cual muchas personas han dejado su huella. Por ello, creo conveniente dedicar unas palabras de agradecimiento a las mismas.Me gustaría dedicar mis primeras palabras a mis directores, quienes me han otorgado la posibilidad de realizar esta tesis. A Ricardo y Pablo, por introducirme en el proyecto que ha hecho posible esta tesis. En especial, a Valery, por su consejo y ayuda en el día a día. Por ser la artífice de un grupo en el cual los jóvenes podamos crecer como investigadores, y aspirar a todas nuestras metas. Gracias.A quienes han compartido su conocimiento conmigo. A Adri, por apoyar mis primeros pasos en el ámbito de la visión por computador y el deep learning. A Jose Dolz, por abrirme nuevos caminos durante mi estancia en Montreal. Asímismo, querría acordarme del AlgoTeam de ContinUse, por apostar por mi y darme una invaluable formación en mis primeros años de investigador.Gracias a todos mis compañeros del CVBLAB por compartir ideas, bromas y amistad. A todos los que han compartido su camino con el mío: Gabri, Elena, Rocío, Cristian, Laëti y Claudio. A los que inician ahora el suyo: Alejandro, Pablo e Ilán. Mi eterna gratitud a quien más horas ha dedicado a esta tesis, día y noche, de forma desinteresada: gracias DGX, y gracias GPU-4.Finalmente, querría tener un agradecimiento especial a mi familia. A mis padres y hermano, por educarme, cuidarme, y apoyarme en todos mis proyectos. A Anny, por aguantar mi pasión, en ocasiones obsesiva, en este trabajo; por escucharme y aconsejarme siempre que lo necesitaba; y por acompañarme en este camino, y en los que están por venir. Os quiero.

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Rail Surface Defect Detection and Severity Analysis Using CNNs on Camera and Axle Box Acceleration Data

Jahan,

Lähns,

Baasch

et al. 2024

Lecture Notes in Mechanical Engineering

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Detecting corrugation defects in harbour railway networks using axle-box acceleration data

Cited by 9 publications

References 16 publications

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part II: Track and Vehicle

Learning from limited labelled data: contributions to weak, few-shot, and unsupervised learning

Rail Surface Defect Detection and Severity Analysis Using CNNs on Camera and Axle Box Acceleration Data

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