CARES: Cloud-based Aircraft Readiness Enhancement and Sustainment with Digital Twins

For aviation systems, digital twin (DT) technology has a potential to improve supply chain integrity, flight safety, in-flight service, foreign object detection, and condition-based maintenance. It helps to create a living model of physical asset for predictive maintenance. IFT proposes to develop an augmented DT model for a Navy aircraft engine that incorporates both data-driven and physics-based modeling approaches. The proposed Cloud-based Aircraft Readiness Enhancement and Sustainment System (CARES) is based on a combination of the data-driven analytics and physics-based modeling. It can mirror the activities of its corresponding physical counterpart with the capabilities of early warning, anomaly detection, maintenance planning, and cost optimization. CARES incorporates three levels of fusion: Data-level Fusion collects and processes dynamic knowledge, such as on-board sensor data, to extract features for the predictive model; Model-level Fusion monitors health condition and detect failures with a fusion of both data-driven and physics-based models for an aircraft product; Decision-level Fusion implements the diagnostic and prognostic analysis to derive a list of corresponding prioritized recommended actions including predictive and preventive maintenance or urgent corrective actions. A cloud-based Hardware-in-the-Loop (HITL) DT is developed to allow real-time monitoring of performance and facilitate in-flight service of a selected naval aircraft product.