Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

Featured Application Online wheel condition monitoring for condition based and predictive maintenance. Continuous wheel condition monitoring is indispensable for the early detection of wheel defects. In this paper, we provide an approach based on cepstral analysis of axle-box accelerations (ABA). It is applied to the data in the spatial domain, which is why we introduce a new data representation called navewumber domain. In this domain, the wheel circumference and hence the wear of the wheel can be monitored. Furthermore, the amplitudes of peaks in the navewumber domain indicate the severity of possible wheel defects. We demonstrate our approach on simple synthetic data and real data gathered with an on-board multi-sensor system. The speed information obtained from fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU) data is used to transform the data from time to space. The data acquisition was performed with a measurement train under normal operating conditions in the mainline railway network of Austria. We can show that our approach provides robust features that can be used for on-board wheel condition monitoring. Therefore, it enables further advances in the field of condition based and predictive maintenance of railway wheels.

Automated summary

This paper presents an approach based on cepstral analysis of axle-box accelerations for continuous wheel condition monitoring. The introduction of the navewumber domain allows for monitoring wheel wear and detecting possible defects based on peak amplitudes. The method is validated on synthetic and real data, providing robust features for on-board wheel condition monitoring and further advancing predictive maintenance of railway wheels.