  Railway axle maintenance is critical to avoid catastrophic failures.
Nowadays, condition monitoring techniques are becoming more prominent in the
industry to prevent enormous costs and damage to human lives. This paper
proposes the development of a railway axle condition monitoring system based on
advanced 2D-Convolutional Neural Network (CNN) architectures applied to
time-frequency representations of vibration signals. For this purpose, several
preprocessing steps and different types of Deep Learning (DL) and Machine
Learning (ML) architectures are discussed to design an accurate classification
system. The resultant system converts the railway axle vibration signals into
time-frequency domain representations, i.e., spectrograms, and, thus, trains a
two-dimensional CNN to classify them depending on their cracks. The results
showed that the proposed approach outperforms several alternative methods
tested. The CNN architecture has been tested in 3 different wheelset
assemblies, achieving AUC scores of 0.93, 0.86, and 0.75 outperforming any
other architecture and showing a high level of reliability when classifying 4
different levels of defects.
