Intelligent Strain Sensing on a Smart Composite Wing Using Extrinsic Fabry-Perot Interferometric Sensors and Neural Networks


Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three Extrinsic Fabry-Perot Interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feed-forward neural networks trained using back-propagation training algorithm. This mapping provides a method to predict stall condition by comparing the strain available in real time and the predicted strain by the trained neural network.

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Conference Proceeding



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Journal Title

Proceedings of the International Joint Conference on Neural Networks