A decision support approach for condition-based maintenance of rails based on big data analysis

Ali Jamshidi, Siamak Hajizadeh, Zhou Su, Meysam Naeimi, Alfredo Núñez*, Rolf Dollevoet, Bart De Schutter, Zili Li

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)
68 Downloads (Pure)

Abstract

In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated estimation of the rail health conditions to support the maintenance decisions for a given time period. An expert-based system is defined to analyse interdependency between the prior knowledge of the track (defined by influential factors) and the surface defect measurements over the rail. When the rail health conditions is computed, the different track segments are prioritized, in order to facilitate grinding planning of those segments of rail that are prone to critical conditions. In this paper, real-life rail conditions measurements from the track Amersfoort-Weert in the Dutch railway network are used to show the benefits of the proposed methodology. The results support infrastructure managers to analyse the problems in their rail infrastructure and to efficiently perform a condition-based maintenance decision making.
Original languageEnglish
Pages (from-to)185-206
JournalTransportation Research Part C: Emerging Technologies
Volume95
DOIs
Publication statusPublished - 2018

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Axle Box Acceleration (ABA) system
  • Condition-based maintenance
  • Decision support system
  • Fuzzy inference system
  • Rail surface defects

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