Automatic Detection of Squats in Railway Infrastructure

• Published in: IEEE transactions on intelligent transportation systems Vol. 15; no. 5; pp. 1980 - 1990
• Main Authors: Molodova, Maria, Zili Li, Nunez, Alfredo, Dollevoet, Rolf
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation; Axle box acceleration (ABA); Defects; Degradation; Frequency measurement; Insulation life; Mathematical models; Monitoring; Rail transportation; rail transportation maintenance; Railroads; Rails; Railway engineering; railway monitoring; Railways; Signal processing; Surface defects; surface defects on railway rails; Wavelet transforms; surface defects on railway rails; Axle box acceleration (ABA); railway monitoring; rail transportation maintenance
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2014.2307955

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. The hit rate for small rail surface defects was 78%.