Print Email Facebook Twitter A decision support approach for condition-based maintenance of rails based on big data analysis Title A decision support approach for condition-based maintenance of rails based on big data analysis Author Jamshidi, A. (TU Delft Railway Engineering) Hajizadeh, S. (TU Delft Railway Engineering) Su, Z. (TU Delft Team Bart De Schutter) Naeimi, M. (TU Delft Railway Engineering) Nunez, Alfredo (TU Delft Railway Engineering) Dollevoet, R.P.B.J. (TU Delft Railway Engineering) De Schutter, B.H.K. (TU Delft Team Bart De Schutter) Li, Z. (TU Delft Railway Engineering) Date 2018 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. Subject Axle Box Acceleration (ABA) systemCondition-based maintenanceDecision support systemFuzzy inference systemRail surface defects To reference this document use: http://resolver.tudelft.nl/uuid:2052c042-f618-4ac4-870b-63204a5527a4 DOI https://doi.org/10.1016/j.trc.2018.07.007 Embargo date 2019-01-26 ISSN 0968-090X Source Transportation Research. Part C: Emerging Technologies, 95, 185-206 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. Part of collection Institutional Repository Document type journal article Rights © 2018 A. Jamshidi, S. Hajizadeh, Z. Su, M. Naeimi, Alfredo Nunez, R.P.B.J. Dollevoet, B.H.K. De Schutter, Z. Li Files PDF 1_s2.0_S0968090X18309859_main.pdf 2.53 MB Close viewer /islandora/object/uuid:2052c042-f618-4ac4-870b-63204a5527a4/datastream/OBJ/view