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

Kakkattukuzhy M. Isaac, Missouri University of Science and Technology
Donald C. Wunsch, Missouri University of Science and Technology
Steve Eugene Watkins, Missouri University of Science and Technology
Rohit Dua, Missouri University of Science and Technology
V. M. Eller

This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1691

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Abstract

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 feedforward neural networks trained using a backpropagation training algorithm. This mapping provides a method to predict the stall condition by comparing the strain available in real time and the predicted strain by the trained neural network.