The project AMONTRACK focused on the development and implementation of methods and tools for acoustic monitoring of railway tracks. For a well-functioning railway infrastructure, it is essential to detect faults in an early stage. The potential consequences of advanced track damage can be damage to rolling stock, high maintenance costs, or even the interruption of transport corridors. Consequently, the social costs due to damages to the track system can be high. At the same time, the railway transport system is a crucial contributor to achieving sustainability in transport and the reduction of CO2 emissions.
Track damage often starts with small irregularities on the rail surface. Over time these irregularities grow to severe faults and eventually to track failures. As the control of modern track infrastructure by humans walking along tracks is impractical, and the detection of early-stage defects by visual inspection is difficult, an automated way of detecting localized rail and track default is important. The possibility of using acoustic track monitoring for this purpose has been investigated in AMONTRACK.
The goal was to identify defects on the track in an automated way from acoustic on-board measurements on a specially equipped railway car. To reach this goal, the project focused on four research objectives:
1. Identification of acoustics signatures of localized rail and track faults in the measurement signals based on the type of fault and its severity.
2. Understanding of the influence of different track design parameters and the rail roughness on the measured axle box acceleration and the sound pressure over a bogie of the railway car.
3. Clarification of what information can be extracted from the measured data by going the inverse way from the measurements to the source.
4. Suggestion of an algorithm to detect faults and to extract information from the measured signals based on pattern recognition.
In the project, an algorithm to detect a rail surface defect called ‘squat’ from measured axle box acceleration on-board the train has been implemented. The algorithm is based on machine learning and has been tested in a full-scale experiment on a track with well-documented squats. The approach has shown to work well and was able to identify squats with sufficiently high accuracy. The results indicate that the proposed method is worthwhile to be developed further for use in the field to detect squats in an automated way, resulting in more efficient track monitoring.
