scholarly journals Measurement Improvement of Distributed Optical Fiber Sensor via Lorenz Local Single Peak Fitting Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1166
Author(s):  
Bin Liu ◽  
Jianping He ◽  
Shihai Zhang ◽  
Yinping Zhang ◽  
Jianan Yu ◽  
...  

Brillouin frequency shift (BFS) of distributed optical fiber sensor is extracted from the Brillouin gain spectrum (BGS), which is often characterized by Lorenz type. However, in the case of complex stress and optical fiber self damage, the BGS will deviate from Lorenz type and be asymmetric, which leads to the extraction error of BFS. In order to enhance the extraction accuracy of BFS, the Lorenz local single peak fitting algorithm was developed to fit the Brillouin gain spectrum curve, which can make the BSG symmetrical with respect to the Brillouin center frequency shift. One temperature test of a fiber-reinforced polymer (FRP) packaged sensor whose BSG curve is asymmetric was conducted to verify the idea. The results show that the local region curve of BSG processed by the developed algorithm has good symmetry, and the temperature measurement accuracy obtained by the developed algorithm is higher than that directly measured by demodulation equipment. Comparison with the reference temperature, the relative measurement error measured by the developed algorithm and BOTDA are within 4% and 8%, respectively.

2010 ◽  
Author(s):  
C. Perrotton ◽  
M. Slaman ◽  
N. Javahiraly ◽  
H. Schreuders ◽  
B. Dam ◽  
...  

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 474
Author(s):  
Fen Xiao ◽  
Mingxing Lv ◽  
Xinwan Li

Brillouin scattering-based distributed optical fiber sensors have been successfully employed in various applications in recent decades, because of benefits such as small size, light weight, electromagnetic immunity, and continuous monitoring of temperature and strain. However, the data processing requirements for the Brillouin Gain Spectrum (BGS) restrict further improvement of monitoring performance and limit the application of real-time measurements. Studies using Feedforward Neural Network (FNN) to measure Brillouin Frequency Shift (BFS) have been performed in recent years to validate the possibility of improving measurement performance. In this work, a novel FNN that is 3 times faster than previous FNNs is proposed to improve BFS measurement performance. More specifically, after the original Brillouin Gain Spectrum (BGS) is preprocessed by Principal Component Analysis (PCA), the data are fed into the Feedforward Neural Network (FNN) to predict BFS.


2010 ◽  
Vol 37 (6) ◽  
pp. 1501-1504
Author(s):  
高存孝 Gao Cunxiao ◽  
朱少岚 Zhu Shaolan ◽  
冯莉 Feng Li ◽  
宋志远 Song Zhiyuan ◽  
曹宗英 Cao Zongying ◽  
...  

2019 ◽  
Vol 46 (4) ◽  
pp. 0410001 ◽  
Author(s):  
章征林 Zhang Zhenglin ◽  
高磊 Gao Lei ◽  
孙阳阳 Sun Yangyang ◽  
张清华 Zhang Qinghua ◽  
曾鹏 Zeng Peng

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