Overview
This project designs and implements predictive maintenance solutions for railway infrastructure and rolling stock, leveraging sensor technologies, AI-driven analytics, and digital twin concepts. By predicting failures before they occur, the approach minimizes downtime, optimizes maintenance schedules, and ensures safe, cost-effective railway operations — monitoring wheels, axles, bearings, braking systems, tracks, switches, and overhead lines.
Predictive Modeling
- Time-series deep learning (LSTMs, Transformers) for degradation trajectories.
- Survival analysis and remaining-useful-life (RUL) estimation for bearings, wheels, and rails.
- Hybrid models combining mechanical wear theory with machine learning.
- Anomaly detection using autoencoders and ensemble methods for early-warning alerts.
Key Deliverables
A predictive-maintenance digital twin for rail systems simulating asset health under varying conditions; RUL forecasting algorithms; a decision-support dashboard for maintenance planners with real-time alerts; and demonstration pilots on selected railway corridors with partner operators.
