Acoustic monitoring of rail faults in the German railway network
Paper i proceeding, 2019

The early detection of rail surface defects such as squats, poor welds, or wheel burns is important to prevent further rail deterioration. In this paper, a methodology for acoustic monitoring of squats in the German railway network is proposed based on the measurement of axle box acceleration (ABA) on the DB noise measurement car (SMW) and the previously developed numerical model WERAN for wheel/rail interaction. Specific characteristics of squats in the ABA signals are determined with the model and verified by pass-by measurements combined with direct geometry measurements of the squats. Based on these re- sults, a logistic regression classifier is devised for the detection of squats in the measured ABA signals of the SMW. Trained with simulated and measured data, the classifier identifies all of the known severe squats and 87% of the known light squats in the measured test data.

Acoustic monitoring

Noise measurement car

Wheel/rail interaction

Axle box acceleration

Time-domain modelling

Machine learning



Astrid Pieringer

Chalmers, Arkitektur och samhällsbyggnadsteknik, Teknisk akustik

Matthias Stangl

DB Systemtechnik GmbH

Jörg Rothhämel

DB Systemtechnik GmbH

Thorsten Tielkes

DB Systemtechnik GmbH

13th International Workshop on Railway Noise
Ghent, Belgium,


Hållbar utveckling




Teknisk mekanik

Strömningsmekanik och akustik

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