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...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2022
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/157513 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| الملخص: | 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 |
|---|