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-actuatedinstrumented 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 first network extracts the harmonics from the strain profile and the second predicts the strain levels through harmonic analysis extracted. INTRODUCTION The choice of particular sensor types for a given sensing application depends upon the parameter being measured and the physical properties of the sensor. The parameter being measured can be strain, temperature, pressure, or force on the structure. Fiber optic sensors have gained importance in recent years and have been used in a variety of structural applications including strain sensing and damage detection [1]-[3]. These sensors integrated with composite structures have been an active area of research in recent years [4]-[7]. However some sensors exhibit non-linear output, which poses a requirement on processing capabilities and a processor must accomplish these tasks quickly and efficiently to make the smart structure an on-line system. Artificial neural networks have attracted increasing attention, in recent years due to their capabilities including pattern recognition, classification and function approximation. For large monitoring systems having numerous built-in sensors (and actuators), real time operation requires high computing speeds. Artificial neural networks have parallel computing architectures, and when implemented in hardware, can quickly process multiple inputs [8]. Neural networks can learn to process data one way, and when conditions change, the processing can adopt to new conditions. They have been extensively used for health monitoring, which involves damage assessment, fatigue monitoring, delamination