This session explores the latest developments in Structural Health Monitoring (SHM) within the aeronautics industry, focusing on the transition from raw sensor data to actionable condition-based maintenance (CBM) strategies. 

Presenters will highlight cutting-edge innovations in defect detection, identification, and localization, along with damage progression models, life prediction techniques, and predictive modeling that enable real-time assessment of aircraft structural integrity. Topics may include, but are not limited to, the integration of machine learning and artificial intelligence for predictive maintenance, the challenges of managing large-scale sensor networks, and the role of digital twins in optimizing aircraft lifecycle management. 

The session aims to bridge the gap between research advancements and practical implementation, offering insights for engineers, researchers, and industry professionals working to enhance aeronautical systems reliability and performance.