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Spectral Splitting Sensing Using Optical Fiber Bragg Grating for Spacecraft Lateral Stress Health Monitoring
- Source :
- Applied Sciences, Vol 13, Iss 7, p 4161 (2023)
- Publication Year :
- 2023
- Publisher :
- MDPI AG, 2023.
-
Abstract
- Optical fiber sensing is a promising detection method for spacecraft health monitoring, since optical fiber sensors are lightweight, small in size, easy to integrate and immune to electromagnetic interference. As a significant optical sensor, fiber Bragg gratings (FBG) are widely used for force sensing because of their axial strain characteristics. However, it is necessary to detect not only one-dimensional strain but also plane strain and its deformation in order to comprehensively evaluate the condition of the structure. Therefore, it is very important to analyze the reflection spectrum of FBG under lateral stress. When FBG are subjected to lateral stress, the refractive index of the waveguide in the x and y directions changes, resulting in a birefringence phenomenon. This result causes the reflection spectrum of FBG to split into two peaks. In this paper, a transverse stress detection method based on spectral split sensing for the fiber Bragg grating is proposed, intended for monitoring spacecraft–small particle collisions. The FBG local lateral stress detection system is designed and verified by experiments. The wavelength pressure correlation is established in the experiment by adjusting the number of weights to change the lateral pressure on the FBG. The loading range of FBG lateral pressure is 4.0–7.0 N, the step size is 0.5 N, and round-trip measurement is carried out four times. The wavelengths of the peak and split point of the FBG reflection spectrum are recorded. The experimental results show that FBG’s split point and right peak pressure sensitivities are 16.57 pm/N and 45.14 pm/N, respectively. The spectral splitting phenomenon can be applied in spacecraft structure health monitoring systems and has certain reference value for the simplification of sensor systems.
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 13
- Issue :
- 7
- Database :
- Directory of Open Access Journals
- Journal :
- Applied Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.6beabe46b239413bada31e0ad41be059
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/app13074161