Satellite Digital Twins

Digital twins were initially introduced in 2002 for product life cycle management, and it has become an emerging technology that has generated growing interest and applications in many fields, including healthcare, agriculture, aerospace engineering, urban planning and development, Earth science, and industry 4.0. This presentation focuses on Satellite Digital Twins (SDT), initially proposed in 2012. An SDT is a digital representation of space and ground assets in space missions in which data models represent satellite and ground system states with predictive capability. It forms a feedback loop with its ground and space assets for optimizing satellite operations: SDTs are synchronized with its space and ground assets with the latest and historical data to perform data model recalibration with data training algorithms so that data models are adaptive to the latest operation states and generate actionable information for corrective actions, such as command procedures, based on model-based monitoring, engineering analysis, and simulations. Hierarchical component architecture for SDTs provides reusability, scalability, and extensibility, enabling rapid deployment in different missions. The Timed Finite State Machine (TSFM) and the fusion of telemetry, mission planning, and log message data for space and ground assets provide a state equation associating satellite state profiles with event triggers for satellite operations with operation events. It provides a formalism for more proactive and dynamic monitoring and model-based high-fidelity simulation. The highly diverse data types and large data volumes for satellite telemetry require efficiency, accuracy, and robustness for data training in real-time or near real-time environments. The innovative solution to address these requirements currently deployed in the GOES-R ground system is presented.