Vibration analysis using extrinsic Fabry-Perot interferometric sensors and neural networks
Abstract
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.
Document Type
Conference Proceeding
Publication Date
12-1-2002
Recommended Citation
Dua, Rohit, Donald C. Wunsch, and Steve Eugene Watkins. 2002. "Vibration Analysis Using Extrinsic Fabry-Perot Interferometric Sensors and Neural Networks." Presented at Artificial Neural Networks in Engineering Conference, ANNIE 2002 Nov. 10-13, 2002 St. Louis, MO.
Journal Title
Intelligent Engineering Systems Through Artificial Neural Networks
