Reference
A. Núñez, J. Hendriks, Z. Li, B. De Schutter, and R. Dollevoet,
"Facilitating maintenance decisions on the Dutch railways using big data: The
ABA case study,"
Proceedings of the 2014 IEEE International
Conference on Big Data, Washington, DC, pp. 48-53, Oct. 2014.
Abstract
This paper discusses the applicability of Big Data techniques to facilitate
maintenance decisions regarding railway tracks. Currently, in different
countries, a huge amount of railway track condition-monitoring data is being
collected from different sources. However, the data are not yet fully used
because of the lack of suitable techniques to extract the relevant events and
crucial historical information. Thus, valuable information is hidden behind a
huge amount of terabytes from different sensors. In this paper, the conditions
of the 5V's of Big Data (Volume, Velocity, Variety, Veracity and Value) in
railway monitoring systems are discussed. Then, general methods that can be
applied to facilitate the decision of efficient railway track maintenance are
proposed for railway track condition monitoring. As a benchmark, axle box
acceleration (ABA) measurements in the Dutch tracks are used, and generic
reduction formulations to address new relevant information and handle failures
are proposed.
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BibTeX
@inproceedings{NunHen:15-014,
author = {N{\'{u}}{\~{n}}ez, Alfredo and Hendriks, Jurjen and Li, Zili
and De Schutter, Bart and Dollevoet, Rolf},
title = {Facilitating Maintenance Decisions on the {Dutch} Railways
Using Big Data: {The} {ABA} Case Study},
booktitle = {Proceedings of the 2014 IEEE International Conference on Big
Data},
address = {Washington, DC},
pages = {48--53},
month = oct,
year = {2014}
}