Vibration analysis using extrinsic Fabry-Perot interferometric sensors and neural networks


An Extrinsic Fabry-Perot interferometric (EFPI) sensor attached to a vibrating structure will see a sinusoidal strain. Harmonic analysis on this strain yields well defined harmonics. Strain level measurement, on a periodically-actuated-instrumented structure, can provide information about the health of that structure. This approach can form a smart health monitoring system for composite structures. A simple demodulation system employing artificial neural networks (ANN) was used to extract harmonics and predict the maximum strain level on a smart composite beam. This paper deals with the computer simulation of the sinusoidal strain and implementation of the demodulation system. The system employs two back-propagation neural networks. The frost network extracts the harmonics from the strain profile and the second predicts the strain levels through harmonic analysis extracted.

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Conference Proceeding

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Journal Title

Intelligent Engineering Systems Through Artificial Neural Networks