Probabilistic defect-based risk assessment approach for rail failures in railway infrastructure

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14 Citations (Scopus)
41 Downloads (Pure)

Abstract

This paper develops a defect-based risk analysis methodology for estimating rail failure risk. The methodology relies on an evolution model addressing the severity level of rail surface defect, called squat. The risk of rail failure is assessed by analyzing squat failure probability using a probabilistic analysis of the squat cracks. For this purpose, a Bayesian inference method is employed to capture a robust model of squat failure probability when the squat becomes severe. Moreover, an experimental correlation between squat visual length and squat crack depth is obtained in order to define four severity categories. Relying on the failure probability and the severity categories of the squats, risk of future failure is categorized in three different scenarios (optimistic, average and pessimistic). To show the practicality and efficiency of the proposed methodology, a real example is illustrated.
Original languageEnglish
Title of host publicationIFAC-PapersOnLine
Subtitle of host publicationProceedings of the 14th IFAC Symposium on Control in Transportation Systems (CTS 2016)
EditorsTankut Acarman
Place of PublicationLaxenburg, Austria
PublisherElsevier
Pages73-77
Volume49-3
DOIs
Publication statusPublished - 2016
Event14th IFAC Symposium on Control in Transportation Systems - ITU Faculty of Architecture, Istanbul, Turkey
Duration: 18 May 201620 May 2016
http://www.cts2016.org/en/

Publication series

NameIFAC-PapersOnLine
PublisherIFAC-Elsevier
Number3
Volume49
ISSN (Print)2405-8963

Conference

Conference14th IFAC Symposium on Control in Transportation Systems
Abbreviated titleCTS 2016
CountryTurkey
CityIstanbul
Period18/05/1620/05/16
Internet address

Keywords

  • imbalance data
  • semi-supervised learning
  • rail image data
  • rail defect detection

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