Machine learning models on demodulation of FBG sensors
Pressure, acceleration, vibration, strain, and temperature are all often measured with Fiber Bragg Grating (FBG) sensors. Spectral overlapping in the wavelength domain can occur in a multiplexed FBG network. The main difficulty that will be explored in this project is demodulating a single FBG...
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| 主要作者: | |
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| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/157513 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Pressure, acceleration, vibration, strain, and temperature are all often measured with
Fiber Bragg Grating (FBG) sensors. Spectral overlapping in the wavelength domain
can occur in a multiplexed FBG network. The main difficulty that will be explored in
this project is demodulating a single FBG wave from a made by mixing spectrum.
To determine the center Bragg wavelengths of each detector in the overlapping
condition, the lowest technique, linear regression used in this project.
With a root mean square error of 0.21 pm and an average testing duration of 0.8
milliseconds, a 30 layers residual |
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