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Machines
Special Issues
High-Speed Railway Systems Technology
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High-Speed Railway Systems Technology

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 2339

Special Issue Editor

Dr. Cecília Vale

E-Mail Website
Guest Editor
Transportation Infrastructures Division, Department of Civil Engineering, Faculty of Engineering of University of Porto, Porto, Portugal
Interests: railway engineering; dynamics and maintenance; optimization methodologies for infrastructures; infrastructure asset management

Special Issue Information

Dear Colleagues,

High-speed rail is a comfortable, safe, flexible, and environmentally sustainable mode of transport that contributes socioeconomic benefits.

This Special Issue aims to show the research progress of high-speed railway and train technology around the world, to improve the exchange of relevant and new methodologies and achievements, and to promote the progress and development of high-speed railway systems.

Topics of interest include but are not limited to:

  • Inovative technologies for high-speed railway infrastructure;
  • Inovative high-speed train technology;
  • Wheel–rail contact in high-speed operations;
  • Dynamic response of high-speed trains;
  • Track dynamics;
  • Running safety assessement of high-speed trains;
  • Inteligent condition-based monitoring for high-speed trains and tracks;
  • Optimal scheduling of maintenance actions.

Dr. Cecília Vale
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • high-speed railway infrastructure
  • high-speed trains
  • wheel–rail contact
  • safety
  • condition-based monitoring
  • maintenance

Published Papers (1 paper)

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Research

26 pages, 15575 KiB  
Article
Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science
by Nelson Traquinho, Cecília Vale, Diogo Ribeiro, Andreia Meixedo, Pedro Montenegro, Araliya Mosleh and Rui Calçada
Machines 2023, 11(10), 981; https://doi.org/10.3390/machines11100981 - 23 Oct 2023
Viewed by 1766
Abstract
Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical [...] Read more.
Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable. Full article
(This article belongs to the Special Issue High-Speed Railway Systems Technology)
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