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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.

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Modern maintenance strategies for railway tracks rely more and more on data acquired with low-cost sensors installed on in-service trains. This quasi-continuous condition monitoring produces huge amounts of data, which require appropriate processing strategies. Deep learning has become a promising tool in analyzing large volumes of sensory data. In this work, we demonstrate the potential of artificial intelligence to analyze railway track defects. We combine traditional signal processing methods with deep convolutional autoencoders and clustering algorithms to find anomalies and their patterns. The methods are applied to real world data gathered with a multi-sensor prototype measurement system on a shunter locomotive operating on the industrial railway network of the inland harbor of Braunschweig (Germany). This work shows that deep learning methods can be applied to find patterns in railway track irregularities and opens a wide area of further improvements and developments.

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