Condition monitoring of railway track surfaces is crucial for ensuring the safety, operational efficiency, and effective maintenance of railway systems. This work presents a data-driven modelling and an experimental methodology for identifying and classifying contaminants on railway tracks using vibration analysis and artificial intelligence techniques. In this study, the railway dynamics were physically simulated using a 1:20 scaled test rig, where the rails were treated with various contaminants (oil, water, and sand), and the resulting vehicle vibrations were recorded by on-board accelerometers and gyroscopes. To construct the predictive model, a hybrid architecture was designed integrating Short-Time Fourier Transform (STFT) for time-frequency feature extraction and a multi-channel Convolutional Neural Network (CNN) for pattern recognition. Initial results indicate that accelerometer data, particularly from longitudinal and lateral vibrations, are more effective than gyroscope data for classifying certain contaminants. To enhance classification robustness, this work introduces a multi-channel CNN that simultaneously processes the most informative signals, leading to a significant improvement in detection accuracy across all tested contaminants. This study validates the effectiveness of the proposed methodology as a robust and reliable solution for contaminant detection, while also confirming the utility of the scaled testbed as a valuable platform for future research in railway dynamics.

Railway wheel wear poses major safety and maintenance challenges, yet accurate predictive models are limited by a lack of synchronized dynamic and wear data from scaled systems. This article presents an integrated methodology to generate a correlative dataset of dynamic parameters and wear progression on the wheels of a 1:20 scale railway system. The experimental approach combines synchronized multisensor data acquisition with sequential microscopic imaging under controlled operating conditions, specifically during braking maneuvers at track transitions. The resulting publicly available dataset enables direct analysis of how operational factors influence physical degradation.

The main challenge in railway predictive maintenance is balancing quantitative precision with interpretability, given the scarcity of experimental data linking dynamics and wear. In this study, a 1:20 scale test rig equipped with accelerometers, a gyroscope, and microscopy generated a multimodal dataset comprising 2460 cycles, synchronizing vibration data (1 kHz) with sequential images of the rolling surface. Two models were developed and compared: (1) Principal Component Analysis and Support Vector Machines (PCA+SVM) to classify four wear states, and (2) a Fully Connected Neural Network (FCNN) for Remaining Useful Life (RUL) regression. The PCA+SVM model achieved 84% accuracy and, through factor loadings, identified high-frequency energy (200–500 Hz) and kurtosis as physical precursors to fatigue. The FCNN model yielded a Mean Absolute Error (MAE) of 100 cycles (NMAE = 4.06%). Together, these results show that PCA+SVM is well-suited for interpretable diagnostics, while FCNN excels at quantitative forecasting, laying the groundwork for a hybrid predictive system.