SBIR/STTR Award attributes
Global Technology Connection (GTC), in collaboration with its university partner, proposes to build a digital twin for the P-8 aircraft that continuously monitors the health of multiple components on the aircraft, and uses a combination of system level prognostics methods and machine learning methods to predict overall aircraft performance based on degrading components in the aircraft, and develops signal analysis and classifier-based methods to detect and isolate faults before they result in catastrophic situations. The digital twin will fuse data operational collected from the aircraft with physics-based models of an important aircraft subsystem, the Integrated Drive Generator (IDG) for predictive health metrics (fault conditions and IDG Remaining Useful Life (RUL) to support informed decision making for condition based maintenance. These metrics enable an early warning system to facilitate optimal maintenance and support a continual mission-ready state. We will leverage our prior work on physics-based and data-driven modeling in support of diagnostics and prognostic and health management (PHM) for aircraft generators and digital twin development to ensure rapid success.
