Improved Multi-Grating Filtering Demodulation Method Based on Cascading Neural Networks for Fiber Bragg Grating Sensor

Abstract

In recent decades, fiber Bragg grating (FBG) sensors have proven useful for structural health monitoring. An accurate and low-cost FBG demodulation method is needed to improve the performance of these sensors in structural-monitoring applications. This paper presents an improved method of matched multi-FBG-filtering demodulation that uses two cascading artificial neural networks (ANNs). The first net is used to select the matched-FBG, and the second net is used to demodulate the sensing signal from the FBG sensor. Several algorithms were tested for training the ANNs. The scaled conjugate gradient backpropagation algorithm proves to be the best algorithm for training the first ANN, and the one-step-secant backpropagation algorithm is most suitable for training the second ANN. Errors in the cascading ANNs can be decreased by adjusting the difference in wavelength between the matched FBGs and varying the algorithms used in the ANNs. When the difference in wavelength is 0.2271 nm, the maximum errors returned with test sets using the optimal algorithms are −10.39 pm and −10.11 μ ϵ for wavelength and strain, respectively. The ANNs prove to be generalizable, given in our results.