Methods and Systems for Hybrid Digital Twin Driven Health Predictions for Aircraft Sub-systems

In the aerospace industry, modern aircraft are increasingly equipped with a growing number of sensors, which enable the development of predictive maintenance solutions utilizing data-driven diagnostic and prognostic (D&P) techniques to enhance operational availability and reduce maintenance costs. However, constructing a purely data-driven D&P solution requires a substantial amount of run-to-fail sensor data, which is often unavailable for highly reliable and safety-critical aircraft systems. This limitation restricts the applicability of purely data-driven D&P solutions for aircraft subsystems. To address this limitation, we developed a novel Hybrid Digital Twin framework that integrates physics-based subsystem models with sensor data, enabling enhanced feature generation for improved fault diagnostics and prognostics. Our approach simultaneously estimates both design and health-related parameters, facilitating accurate model calibration even when some of design data is not available. Sensor features enhanced with estimated health-related parameters enable more accurate data-driven diagnostics and prognostics solutions of a sub-system or a component. The framework is demonstrated on key subsystems of the aircraft Environment Control System (ECS), including the Heat Exchanger and Centrifugal Compressor. Various parameter estimation techniques including nonlinear least squares, particle swarm optimization, and extended Kalman filter, Unscented Kalman filter, Physics-Informed Neural Networks, etc., are evaluated. This Hybrid Digital Twin approach offers a promising pathway for more accurate, robust and scalable health management of aircraft subsystems having limited operational data.