Abstract
Fiber Bragg gratings (FBGs) are among the most utilized fiber optic sensors due to their high sensitivity toward physical parameters, which makes them preferable for various sensing applications. In this theoretical research work, an apodized FBG is designed to eliminate the side lobes from the reflectivity spectrum for the sensitivity estimation of physical parameters including temperature, strain, and pressure. Bragg wavelength changes due to the effects of measurands as FBG works on the principle of Bragg wavelength shift. The sensitivity of the measurand is measured by analyzing the changes that occur in the Bragg wavelength due to the impact of measurands. A strong linearity has been obtained for the sensitivity measurement of temperature, strain, and pressure for the designed apodized FBG. The Support vector machine (SVM), a supervised machine learning (ML) model, is implemented with different kernel functions for the predictive analysis of physical parameters for the credibility of the designed apodized FBG sensor to achieve improved sensing outcomes, particularly to work in a hazardous environment in case of any adverse scenario of any physical parameter. The predictive analysis performance of the SVM with RBF (radial basis function) kernel is observed better compared to the PUK (Pearson VII universal kernel) kernel as indicated by the high R2 value.
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Data Availability Statement
The datasets implemented during this research work are available from the corresponding author as per the request.
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The authors are grateful to the Department of Electronics and Communication Engineering (ECE), Birla Institute of Technology Mesra for extending all the research facilities.
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Mandal, H.N., Sidhishwari, S. Design of an Apodized Fiber Bragg Grating Sensor for Sensitivity Analysis of Physical Parameters using Support Vector Machine. Braz J Phys 54, 58 (2024). https://doi.org/10.1007/s13538-024-01431-z
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DOI: https://doi.org/10.1007/s13538-024-01431-z