• Gajji Haripriya
• Dr.M.Jayanthi

Wheel defects in railway systems are a major safety concern and contribute to increased maintenance costs and service disruptions. Traditional inspection methods often rely on sensors or manual observation, which can be time-consuming and expensive. This study investigates the use of machine learning techniques Support Vector Machine (SVM), Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) to detect defects in railway wheels using only extracted features from available data. Among the evaluated models, Decision Tree and Random Forest classifiers demonstrated the most consistent and accurate performance. This sensor-free, data-driven approach provides a cost-effective and scalable solution for enhancing railway safety through intelligent defect detection.

Railway Wheel Defect Detection Using Machine Learning Techniques

"Railway Wheel Defect Detection Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.327-333, May-2025, Available :http://www.jetir.org/papers/JETIRGV06048.pdf

"Railway Wheel Defect Detection Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. pp327-333, May-2025, Available at : http://www.jetir.org/papers/JETIRGV06048.pdf