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

Journal article (2018)

Authors

A. Jamshidi Railway Engineering - CEG
S. Hajizadeh Railway Engineering - CEG
Z. Su Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
M. Naeimi Railway Engineering - CEG
Alfredo Nunez Railway Engineering - CEG
R.P.B.J. Dollevoet Railway Engineering - CEG
B.H.K. De Schutter Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Z. Li Railway Engineering - CEG
Research Group
Railway Engineering (CEG) (TU Delft)
Copyright: © 2018 A. Jamshidi, S. Hajizadeh, Z. Su, M. Naeimi, Alfredo Nunez, R.P.B.J. Dollevoet, B.H.K. De Schutter, Z. Li
DOI
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https://doi.org/10.1016/j.trc.2018.07.007
Condition-based maintenance Decision support system Rail surface defects Axle Box Acceleration (ABA) system Fuzzy inference system
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Published Date

2018

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Railway Engineering

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.