Machine-Learning-Based Diagnostic Framework for Passive Ultrasonic Detection of Railway Wheel Defects
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...
| Authors | Aashish Shaju, Steve Southward, Mehdi Ahmadian |
|---|---|
| Primary category | cs.LG |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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