  For efficient railway operation and maintenance, the demand for onboard
monitoring systems is increasing with technological advances in high-speed
trains. Wheel flats, one of the common defects, can be monitored in real-time
through accelerometers mounted on each axle box so that the criteria of
relevant standards are not exceeded. This study aims to identify the location
and height of a single wheel flat based on non-stationary axle box acceleration
(ABA) signals, which are generated through a train dynamics model with flexible
wheelsets. The proposed feature extraction method is applied to extract the
root mean square distribution of decomposed ABA signals on a balanced binary
tree as orthogonal energy features using the Hilbert transform and wavelet
packet decomposition. The neural network-based defect prediction model is
created to define the relationship between input features and output labels.
For insufficient input features, data augmentation is performed by the linear
interpolation of existing features. The performance of defect prediction is
evaluated in terms of the accuracy of detection and localization and improved
by augmented input features and highly decomposed ABA signals. The results show
that the trained neural network can predict the height and location of a single
wheel flat from orthogonal energy features with high accuracy.
