Methods and systems for airfoil prognostics using physics-informed machine learning introduce perturbations to a nominal airfoil geometry, simulating potential faults. The perturbed airfoil geometries are processed by physics-informed neural network (PINN) models, trained on geometric and physics-based features, to predict lift coefficients, thereby predicting the health status of the airfoil. A fault probing strategy is applied to assess health status under mission-specific requirements, predicting a remaining useful life and confidence bounds for each mission-specific requirement. Efficient airfoil health prediction, including precise fault simulation, allow for proactive maintenance planning based on the predicted health status of the airfoil.
METHODS AND SYSTEMS FOR AIRFOIL PROGNOSTICS USING PHYSICS-INFORMED MACHINE LEARNING
2024-12-12
Patent
Electronic Resource
English