Transverse load discrimination in Long-Period Fiber Grating via Artificial Neural Network

F. O. Barino, F. S. Delgado, A. B. dos Santos

Abstract


We present a general investigation of a Long-Period Grating (LPG) for transverse strain measurement. The transverse strain sensing characteristics, for instance, the load intensity and azimuthal angle, are analyzed with the data set generated by the long-period grating sensor, which can be detected by using artificial nerural network (ANN). Furthermore, we evaluate and compare the predictive performance of the interrogation model considering the square correlation coefficient (R2) and root mean square error (RMSE). The results indicate that the ANN model could be successfully employed to estimate the load intensity and azimuthal angle.

Keywords


artfificial neural network; long period grating; optical fiber sensor; transverse load

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References


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DOI: http://dx.doi.org/10.1590/2179-10742020v19i11809

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