# Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations
###### 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. c 2021 11 1432 11 11432 11 11432 11 11  
The traditional maintenance strategies of wheelsets are based on the removal of the wheelset at given intervals. However, from a safety, environmental and economic point of view, the early detection of wheel defects is important. Wayside monitoring systems are commonly used to detect faulty wheelsets in service. [PERSON] et al. [7] investigated an envelope spectral analysis approach to detect wheel flats with wayside sensors using a range of 3D simulations based on a train-track interaction model. In contrast to wayside systems, on-board monitoring systems have traditionally been focused on the detection of track defects [8; 9; 10; 11; 12] but are more and more considered for vehicle monitoring [13]. The advantage of on-board monitoring systems is that the wheel is monitored continuously and not only when the vehicle passes a track side monitoring site. This allows for the timely detection of emerging wheel defects [14]. Furthermore, if the on-board monitoring system provides positioning, the occurrence of a wheel defect can be linked to a position on the track. The track at this position can then be inspected and in case a track defect is identified, appropriate maintenance actions can be issued to avoid further damage to the rail and other passing vehicles. Previous studies have also shown that train-borne accelerometers can be used to monitor the wheel. In [15] a methodology is proposed to monitor the wheel diameter by means of onboard vibration measurements. [PERSON] and [PERSON] [16] used wavelet approaches for monitoring rail wheel flat damage from bogie vertical acceleration signatures. Several methods based on the analysis of axle-box acceleration (ABA) have been proposed. [PERSON] et al. [14] used vertical ABA to detect wheel flat defects. In [17], a data driven approach to estimate the length of a wheel flat is proposed. [PERSON] et al. [18] presented a frequency-domain image classification method to analyze wheel flats. In general, 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. Furthermore, supervised machine learning can be used to predict class memberships and their probabilities and to estimate relationships between independent variables, e.g., health status indices, and features extracted from the data. These methodologies are quite powerful. They can approximate complex and non-linear relationships and need little or no a priori information. Therefore, they are often preferred over model-based approaches. However, since machine learning techniques make use of the underlying statistics in the data, they rely on the fact that sufficient data are available.