Comparison of regression methods for transverse load sensor based on optical fiber long-period grating

Abstract

In this work, we report the comparison of regression methods in a long-period grating (LPG) for transverse strain measurement. We analyze the transverse strain sensing characteristics, such as load intensity and azimuthal angle, based on the birefringence effect induced in LPG sensor. Therefore, we employ the different orthogonal responses of the grating to develop regression methods, which allow the estimation of the strain behavior of the LPG sensor. The predictive performances of these interrogation models are compared in terms of square correlation coefficient (R2) and root mean square error (RMSE). Finally, the results indicate that the best method to predict load intensity is the Fourth-Degree Polynomial Fit, whereas the artificial neural network (ANN) model could be successfully employed to predict the azimuthal angle.