Premature and delayed maintenance interventions in the shipping industry hinder Sustainable Development Goals set by the International Maritime Organization. TRANSITION-MODEL seeks to revolutionize maintenance practices through predictive modeling, addressing downtime and environmental threats. It minimizes resource mismanagement and navigates diverse operating conditions. Using Machine Learning and Artificial Intelligence, the project employs offline and real-time algorithms for anomaly detection, degradation modeling, and Remaining Useful Life prediction. Leveraging operational data from a Norwegian research vessel enhances transparency and trust, minimizing computational costs. Hosted by Aalto University's Mechanical Engineering Department, TRANSITION-MODEL aligns with the institution's commitment to cutting-edge research. By integrating technologies, real-world data, and academic expertise, TRANSITION-MODEL propels the shipping industry toward a greener, more efficient future.

2024

2028