Smart detection of wheel defects using artificial intelligence and wayside monitoring system

Mosleh, Araliya; Mohammadi, Mohammadreza; Vale, Cecília; Ribeiro, Diogo; Montenegro, Pedro; Meixedo, Andreia

doi:10.22068/ijrare.322

Volume 10, Issue 2 (12-2023) IJRARE 2023, 10(2): 9-18 | Back to browse issues page

‎ 10.22068/ijrare.322

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Mosleh A, Mohammadi M, Vale C, Ribeiro D, Montenegro P, Meixedo A. Smart detection of wheel defects using artificial intelligence and wayside monitoring system. IJRARE 2023; 10 (2) :9-18
URL: http://ijrare.iust.ac.ir/article-1-322-en.html

Smart detection of wheel defects using artificial intelligence and wayside monitoring system

Araliya Mosleh

, Mohammadreza Mohammadi

CONSTRUCT – LESE, Faculty of Engineering, University of Porto, Porto, Portugal

Abstract: (701 Views)

Given the significant role of the railway sector in transportation, railway managers and operators place great importance on traffic and maintenance costs. While existing track wayside monitoring systems can detect geometric defects in train wheels, like flats, they do not provide a severity assessment. To address this limitation, the WAY4SafeRail project aims to enhance rail safety by assessing the condition of train wheels. As an initial step in employing Artificial Intelligence Techniques, this paper presents a portion of the research conducted within the WAY4SafeRail project, specifically focusing on numerical simulations of wheel defects, in particular wheel flats. The proposed methodology has demonstrated its reliability and cost-effectiveness in identifying wheel defects.

Keywords: Artificial intelligence, damage detection, wheel flat, train-track dynamic interaction, wayside condition monitoring

Full-Text [PDF 814 kb] (338 Downloads)

Type of Study: Research | Subject: Rolling Stock