The rapid advancements in artificial intelligence (AI) and machine learning (ML) are revolutionizing aircraft health management, enabling new practices of maintenance, i.e. condition based maintenance. This special session will explore state-of-the-art AI/ML-driven methodologies for aircraft diagnostics and prognostics, leveraging data analytics to optimize health management methodologies.

Key topics include, but are not limited to:

• AI/ML techniques for fault and damage detection
• Data-driven prognostics and remaining useful life (RUL) estimation
• Digital twins and physics-informed AI for aircraft systems
• Sensor fusion and big data analytics for condition based maintenance
• Real-time health monitoring and decision support systems

This session aims to bring together researchers, industry experts, and practitioners to discuss the latest developments, challenges, and future directions in AI-based aircraft health management. We welcome contributions that highlight innovative approaches, case studies, and practical implementations in aviation applications.

Keywords: AI/ML, diagnostics, prognostics, data analytics, uncertainty quantification