Data-Driven Failure Prediction in Metro Trains: An ML-Based Approach to Predictive Maintenance
This study aims to predict metro-train failures using machine learning (ML) models, specifically focusing on XGBoost and Random Forest. Data from the MetroPT dataset were labeled based on time intervals relative to failure: from the start of the dataset to 24 hours before failure, 24 to 2 hours before, 2 hours prior, a...