Comparison of regression methods for transverse load sensor based on optical fiber long-period grating
| Autor: | Felipe Oliveira Barino, A. Bessa dos Santos, Marco Aurélio Jucá, Felipe S. Delgado, Thiago V. N. Coelho |
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| Rok vydání: | 2019 |
| Předmět: |
Polynomial regression
Optical fiber Materials science Correlation coefficient Mean squared error Applied Mathematics Acoustics 020208 electrical & electronic engineering 010401 analytical chemistry 02 engineering and technology Grating Condensed Matter Physics 01 natural sciences 0104 chemical sciences law.invention Azimuth law Fiber optic sensor 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Instrumentation Intensity (heat transfer) |
| Zdroj: | Measurement. 146:728-735 |
| ISSN: | 0263-2241 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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