Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II
Fiber Bragg grating (FBG) technology has shown a mutation in developing fiber optic-based sensors because of their tiny size, high dielectric strength, distributed sensing, and immunity to high voltage and magnetic field interference. Therefore, FBG sensors significantly improve performance and accu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/21/8203 |
| _version_ | 1797466546061180928 |
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| author | Yasser Elsayed Hossam A. Gabbar |
| author_facet | Yasser Elsayed Hossam A. Gabbar |
| author_sort | Yasser Elsayed |
| collection | DOAJ |
| description | Fiber Bragg grating (FBG) technology has shown a mutation in developing fiber optic-based sensors because of their tiny size, high dielectric strength, distributed sensing, and immunity to high voltage and magnetic field interference. Therefore, FBG sensors significantly improve performance and accuracy in the world of measurements. The reflectivity and bandwidth are the main parameters that can dramatically affect the sensing performance and accuracy. Each industrial application has its requirements regarding the reflectivity and bandwidth of the reflected wavelength. Optimizing such problems with multi-objective functions that might t with each other based on applications’ needs is a big challenge. Therefore, this paper presents an optimization method based on the nondominated sorting genetic algorithm II (NSGA-II), aiming at determining the optimum grating parameters to suit applications’ needs. To sum up, the optimization process aims to convert industrial applications’ requirements, including bandwidth and reflectivity, into the manufacturing setting of FBG sensors, including grating length and modulation refractive index. The method has been implemented using MATLAB and validated with other research work in the literature. Results proved the capability of the new way to determine the optimum grating parameters for fulfilling application requirements. |
| first_indexed | 2024-03-09T18:41:19Z |
| format | Article |
| id | doaj.art-b2f1b414bf5840e58bfb31217026a97d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:41:19Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b2f1b414bf5840e58bfb31217026a97d2023-11-24T06:44:16ZengMDPI AGSensors1424-82202022-10-012221820310.3390/s22218203Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-IIYasser Elsayed0Hossam A. Gabbar1Faculty of Engineering and Applied Science, Ontario Tech University, 2000 Simcoe St. North, Oshawa, ON L1G0C5, CanadaFaculty of Engineering and Applied Science, Ontario Tech University, 2000 Simcoe St. North, Oshawa, ON L1G0C5, CanadaFiber Bragg grating (FBG) technology has shown a mutation in developing fiber optic-based sensors because of their tiny size, high dielectric strength, distributed sensing, and immunity to high voltage and magnetic field interference. Therefore, FBG sensors significantly improve performance and accuracy in the world of measurements. The reflectivity and bandwidth are the main parameters that can dramatically affect the sensing performance and accuracy. Each industrial application has its requirements regarding the reflectivity and bandwidth of the reflected wavelength. Optimizing such problems with multi-objective functions that might t with each other based on applications’ needs is a big challenge. Therefore, this paper presents an optimization method based on the nondominated sorting genetic algorithm II (NSGA-II), aiming at determining the optimum grating parameters to suit applications’ needs. To sum up, the optimization process aims to convert industrial applications’ requirements, including bandwidth and reflectivity, into the manufacturing setting of FBG sensors, including grating length and modulation refractive index. The method has been implemented using MATLAB and validated with other research work in the literature. Results proved the capability of the new way to determine the optimum grating parameters for fulfilling application requirements.https://www.mdpi.com/1424-8220/22/21/8203FBG sensorsgrating parametersNSGA-IIFiber Bragg Gratingreflectivitygrating length |
| spellingShingle | Yasser Elsayed Hossam A. Gabbar Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II Sensors FBG sensors grating parameters NSGA-II Fiber Bragg Grating reflectivity grating length |
| title | Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II |
| title_full | Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II |
| title_fullStr | Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II |
| title_full_unstemmed | Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II |
| title_short | Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II |
| title_sort | enhancing fbg sensing in the industrial application by optimizing the grating parameters based on nsga ii |
| topic | FBG sensors grating parameters NSGA-II Fiber Bragg Grating reflectivity grating length |
| url | https://www.mdpi.com/1424-8220/22/21/8203 |
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