Abstract
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.
Highlights
The condition of train wheels has an impact on the passengers’ comfort, the rolling noise generation and the deterioration of railway infrastructure and, the safety of railway operations
Ulrych [27] found out that if the spectrum of a signal is smooth it maps around the origin in the navewumber domain, while the cepstrum of a periodic impulse sequence, as excited by a wheel irregularity, is an impulse sequence with the same period
The cepstrum shows a acepstrum distinct peak at a navewumber of 3 m length that corresponds the wheel circumference
Summary
The condition of train wheels has an impact on the passengers’ comfort, the rolling noise generation and the deterioration of railway infrastructure and, the safety of railway operations. A trend can be noticed in sensor data analysis and condition monitoring towards analysis techniques based on data driven machine learning approaches They can be used to find patterns, i.e., clusters and for outlier and novelty detection in an unsupervised manner. They identified the power cepstrum as the best instrument to reveal periodic acceleration peaks as those excited by wheel flats We adapt this methodology to the analysis of ABA data for wheel condition monitoring. The main contribution of this research is to introduce a simple, robust and yet precise methodology to extract wheel wear related features from the ABA signals without relying on a priori knowledge or training data.
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