Machine-Learning-Based Diagnostic Framework for Passive Ultrasonic Detection of Railway Wheel Defects

Abstract

Reliable identification of railway wheel defects is important for safety and maintenance. This study develops a machine-learning-based diagnostic framework for multi-class defect identification using passive air-coupled ultrasonic acoustic emission signals. Data were collected from eleven full-scale railway wheelsets representing nine health states. Time- and frequency-domain features were evaluated using Kruskal-Wallis statistical testing and mutual-information analysis to identify the most discriminative indicators. A Random Forest classifier was then trained using the selected features with stratified 5-fold cross-validation. The model achieved a balanced accuracy of approximately 0.66 and a Macro-F1 score of 0.65 across the nine classes. Decay rate, kurtosis, skewness, and envelope low-frequency power emerged as the most influential features, while a compact subset of features retained most of the classification performance. The results demonstrate the feasibility of combining passive ultrasonic sensing, statistical feature selection, and supervised machine learning for non-contact railway wheel defect classification and provide a foundation for future field-deployable inspection systems.