Automatic Detection of Squats in Railway Infrastructure

January 1, 2016 in

Journal Paper


Author(s)
Maria Molodova
Zili Li
Alfredo Núñez
Rolf Dollevoet


ISSN 1524-9050
DOI 10.1109/TITS.2014.2307955

Theme(s)


Journal
IEEE Transactions on Intelligent Transportation Systems
Volume 15, Issue 5, Pages 1980-1990

Publishing date:

Keywords
axle box acceleration, rail transportation maintenance, railway monitoring, surface defects on railway rails

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Abstract

This paper presents an automatic method for detecting railway surface defects called “squats” using axle box acceleration (ABA) measurements on trains. The method is based on a series of research results from our group in the field of railway engineering that includes numerical simulations, the design of the ABA prototype, real-life implementation, and extensive field tests. We enhance the ABA signal by identifying the characteristic squat frequencies, using improved instrumentation for making measurements, and using advanced signal processing. The automatic detection algorithm for squats is based on wavelet spectrum analysis and determines the squat locations. The method was validated on the Groningen–Assen track in The Netherlands and accurately detected moderate and severe squats with a hit rate of 100%, with no false alarms. The methodology is also sensitive to small rail surface defects and enables the detection of squats at their earliest stage.