Abstract
The neural-network-based processing of extrinsic Fabry-Perot interferometric (EFPI) strain sensors was investigated for the special case of sinusoidal strain. The application area is modal or cyclic testing of structures in which the frequency response to periodic actuation must be demodulated. The nonlinear modulation characteristic of EFPI sensors produces well-defined harmonics of the actuation frequency. Relationships between peak strain and harmonic content were analyzed theoretically. A two-stage demodulator was implemented with a Fourier series neural network to separate the harmonic components of an EFPI signal and a backpropagation neural network to predict the peak-to-peak strain from the harmonics. The system performance was tested using theoretical and experimental data. The error for high-strain cases was less than about 10% if at least 12 harmonics were used. The frequency response of an instrumented cantilever beam provided the experimental data. The demodulator processing closely matched the actual strain levels
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