Reference
A. Jamshidi, S. Hajizadeh, Z. Su, M. Naeimi, A. Núñez, R.
Dollevoet, B. De Schutter, and Z. Li, "A decision support approach for
condition-based maintenance of rails based on big data analysis,"
Transportation Research Part C, vol. 95, pp. 185-206, Oct.
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 analyze 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 priorities, 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
analyze the problems in their rail infrastructure and to efficiently perform a
condition-based maintenance decision making.
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BibTeX
@article{JamHaj:18-016,
author = {Jamshidi, Ali and Hajizadeh, Siamak and Su, Zhou and Naeimi,
Meysam and N{\'{u}}{\~{n}}ez, Alfredo and Dollevoet, Rolf and De
Schutter, Bart and Li, Zili},
title = {A Decision Support Approach for Condition-Based Maintenance of
Rails Based on Big Data Analysis},
journal = {Transportation Research Part C},
volume = {95},
pages = {185--206},
month = oct,
year = {2018}
}