Rail Break

Rail break prediction is crucial for railway safety and maintenance, aiming to prevent derailments caused by undetected or underestimated rail damage. Current research focuses on developing advanced predictive models, employing machine learning techniques like recurrent neural networks (RNNs) to forecast crack propagation based on historical data and environmental factors, and image processing methods (e.g., using GLCM and PCA) to detect rail defects from visual inspections. These efforts leverage probabilistic graphical models to assess risk and inform maintenance strategies, ultimately improving railway safety and operational efficiency.