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Detection of Rail Surface Defects based on Axle Box Acceleration Measurements

A Measurement Campaign in Sweden

Conference Paper (2024)
Author(s)

Wassamon Phusakulkajorn (TU Delft - Civil Engineering & Geosciences)

Jurjen Hendriks (TU Delft - Civil Engineering & Geosciences)

Jan Moraal (TU Delft - Civil Engineering & Geosciences)

Chen Shen (TU Delft - Civil Engineering & Geosciences)

Yuanchen Zeng (TU Delft - Civil Engineering & Geosciences)

Siwarak Unsiwilai (TU Delft - Civil Engineering & Geosciences)

Bojan Bogojevic (TU Delft - Civil Engineering & Geosciences)

Matthias Asplund (Trafikverket)

Arjen Zoeteman (ProRail)

Alfredo Nunez (TU Delft - Civil Engineering & Geosciences)

Rolf Dollevoet (TU Delft - Civil Engineering & Geosciences)

Zili Li (TU Delft - Civil Engineering & Geosciences)

Research Group
Railway Engineering
DOI related publication
https://doi.org/10.1007/978-3-032-04774-8_14 Final published version
More Info
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Publication Year
2024
Language
English
Related content
Research Group
Railway Engineering
Pages (from-to)
90-96
Publisher
Springer
ISBN (print)
978-3-032-04773-1
ISBN (electronic)
978-3-032-04774-8
Event
The 10th Transport Research Arena Conference 2024 (2024-04-15 - 2024-04-18), The Royal Dublin Society (RDS), Dublin, Ireland
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Abstract

This work presents the results of a measurement campaign to demonstrate the effectiveness of the axle box acceleration (ABA) technology for detecting rail defects. The measurements were conducted along the Iron Ore line between Sweden and Norway for the IN2TRACK3 project. This line is mostly single-track with passenger-freight mixed traffic and heavy axle load. Historical data and track information data were not considered in this study. By analyzing data acquired from the accelerometers in vertical and longitudinal directions, rail defects were detected in near real-time using big-data analytics. For our validated sections, 100% of rail defects (including squats) were detected using time-frequency analysis and an outlier detection approach. The methodology also allows for identifying priority locations, e.g., defective welds, joints, transition zones, etc., and its use for prescriptive maintenance recommendations is being explored in the framework of the IAM4RAIL project.