Railway Wear: Prediction Using FCNN and Diagnosis Using PCA+SVM

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.