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.


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    Title :

    METHODS AND SYSTEMS FOR AIRFOIL PROGNOSTICS USING PHYSICS-INFORMED MACHINE LEARNING


    Contributors:
    NI KANGYU (author) / LU TSAI-CHING (author)

    Publication date :

    2024-12-12


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    B64C AEROPLANES , Flugzeuge / B64D Ausrüstung für Flugzeuge , EQUIPMENT FOR FITTING IN OR TO AIRCRAFT



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