AI-Enhanced Digital Twin Framework for UAS Component Reliability in Support of In-Time Aviation Safety Management Systems (IASMS)

AlarisPro proposes a novel framework for the integration of AI techniques with UAS component reliability data to create an AI Safety and Reliability (AISR) Digital Twin technology in support of In-Time Aviation Safety Management Systems (IASMS). As UAS operations increase in the National Airspace Systems (NAS), there is a critical need for advanced predictive maintenance capabilities and real-time safety assessment tools. This approach leverages the convergence of high-fidelity sensor data, component degradation models, and advanced AI algorithms to create a robust prognostic framework that can significantly improve operational safety and system reliability. The AISR Digital Twin maintains synchronization with its physical counterpart through continuous data assimilation techniques that optimize the balance between model predictions and sensor observations. The AISR Digital Twin architecture employs transfer learning methodologies to leverage knowledge across different UAS platforms and component types, allowing for rapid adaptation to new systems with limited historical data. This flexibility enables comprehensive safety assessments that account for propagation of component failures through interdependent systems. The integration with IASMS is facilitated through a real-time risk assessment engine that translates AISR Digital Twin outputs into actionable safety metrics. This research demonstrates the significant potential of AI-enhanced AISR Digital Twin technology to transform UAS reliability assessment and advance IASMS capabilities. The framework provides a foundation for transitioning from reactive maintenance paradigms to proactive, condition-based strategies that optimize safety margins while reducing operational costs. This represents a significant advancement in aviation safety management, providing operators, manufacturers, and regulators with unprecedented visibility into current system and component health and future reliability projections.