Detection of Rail Surface Defects based on Axle Box Acceleration Measurements: A Measurement Campaign in Sweden

Conference paper (2024)

Authors

W. Phusakulkajorn Railway Engineering - CEG
J.M. Hendriks Railway Engineering - CEG
R.P.B.J. Dollevoet Railway Engineering - CEG
Z. Li Railway Engineering - CEG
J. Moraal Railway Engineering - CEG
C. Shen Railway Engineering - CEG
Y. Zeng Railway Engineering - CEG
S. Unsiwilai Railway Engineering - CEG
B. Bogojevic Railway Engineering - CEG
Matthias Asplund Trafikverket
A. Zoeteman ProRail
Alfredo Nunez Railway Engineering - CEG
Research Group
Railway Engineering (CEG) (TU Delft)
Rail surface defects Rail defect detection Rail monitoring Axle-box acceleration Intelligent railway infrastructure
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Published Date

15-04-2024

Language

English

Reuse Rights

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Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Railway Engineering

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.