AI-SAFE FlightNet : A Next-Generation Artificial Intelligence and Automation Framework for Predictive Flight Safety, Aircraft Health Intelligence, and Ground Operations Coordination

Abstract

Aviation safety improvement remains a continuous engineering challenge because modern aircraft are sensor-dense, software-intensive, and operationally interconnected, while health and operations data often remain fragmented across onboard, maintenance, dispatch, and ground systems. This fragmentation can delay weak-signal recognition and coordinated response. This paper proposes AI-SAFE FlightNet, a Safety-Aware, Intelligent, Federated, Explainable Flight Automation Network that integrates aircraft data intelligence, onboard edge AI, subsystem digital twins, phase-aware predictive analytics, evidence-grounded coordination, ground operations support, federated fleet learning, and governance by design. The framework supports pilots, engineers, dispatchers, and maintenance controllers without replacing certified human authority or certified aviation systems. A formal model combines sensor, flight-phase, environmental, and maintenance-history variables to estimate risk trajectories, prioritize alerts, and generate auditable response packages. The proposed evaluation uses public engine-degradation and trajectory datasets, synthetic subsystem telemetry, simulated maintenance records, and digital-twin experiments. Expected contributions are earlier warning, improved maintenance readiness, faster aircraft-to-ground coordination, privacy-preserving fleet learning, explainable recommendations, and measurable operational risk reduction. The objective is improved resilience and decision support, not zero risk or unrestricted autonomous control.