This invention describes a system for fine-tuning the balance of an aircraft engine. It works by collecting detailed flight data, including engine vibrations and balance weight settings, from many reference aircraft. This data is used to train a set of artificial intelligence models. When a test aircraft flies, its flight data is fed into these models, which then predict what vibrations the engine would produce. By matching these predictions to the actual vibrations measured on the test aircraft, the system can recommend specific balance weight adjustments based on the configurations of the reference aircraft that produced similar vibration patterns.
Why it matters: Filed when AI models were less mature for complex real-world data. Advances in machine learning algorithms and computational power since 2017 make training and deploying the required artificial neural networks more efficient and accurate for aerospace applications.
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