Distributed strain sensing using slope assisted BOTDA based on virtual Brillouin gain spectrum synthesized by multi-frequency light

2021 ◽  
Author(s):  
Kazuki Hoshino ◽  
Daiki Saito ◽  
Mohd Saiful Dzulkefly Zan ◽  
Yosuke Tanaka
Keyword(s):  
Gain Spectrum ◽  
Strain Sensing ◽  
Brillouin Gain ◽  
Distributed Strain

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 ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Frequency Shift ◽  
Optical Fiber Sensor ◽  
Gain Spectrum ◽  
Fiber Sensor ◽  
Peak Fitting ◽  
Fitting Algorithm ◽  
Distributed Optical Fiber Sensor ◽  
Brillouin Gain ◽  
Distributed Optical Fiber

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.

CHARACTERIZING ANOMALIES IN DISTRIBUTED STRAIN MEASUREMENTS OF CAST-IN-SITU BORED PILES

2016 ◽  
Vol 78 (8-5) ◽  
Author(s):  
Hisham Mohamad ◽  
Bun Pin Tee ◽  
Koh An Ang ◽  
Mun Fai Chong
Keyword(s):  
Load Transfer ◽  
Load Test ◽  
Strain Sensing ◽  
Strain Profile ◽  
A New Technique ◽  
Optical Time ◽  
Distributed Optical Fiber ◽  
Non Destructive ◽  
Distributed Strain

This paper describes the method of identifying typical defects of bored cast-in-situ piles when instrumenting using Distributed Optical Fiber Strain Sensing (DOFSS). The DOFSS technology is based on Brillouin Optical Time Domain Analyses (BOTDA), which has the advantage of recording continuous strain profile as opposed to the conventional discrete based sensors such as Vibrating Wire strain gauges. In pile instrumentation particularly, obtaining distributed strain profile is important when analysing the load-transfer and shaft friction of a pile, as well as detecting any anomalies in the strain regime. Features such as defective pile shaft necking, discontinuity of concrete, intrusion of foreign matter and improper toe formation due to contamination of concrete at base with soil particles, among others, may cause the pile to fail. In this study, a new technique of detecting such defects is proposed using DOFSS technology which can potentially supplement the existing non-destructive test (NDT) methods. Discussion on the performance of instrumented piles by means of maintained load test are also presented

Distributed Strain Sensing in der Geotechnik - Teil 2

Bautechnik ◽  
2018 ◽  
Vol 95 (9) ◽  
pp. 653-657
Author(s):  
Arne Kindler ◽  
Stephan Großwig ◽  
Thomas Pfeiffer
Keyword(s):  
Strain Sensing ◽  
Distributed Strain

Simulating and Designing Brillouin Gain Spectrum in Single-Mode Fibers

2004 ◽  
Vol 22 (2) ◽  
pp. 631-639 ◽  
Author(s):  
Y. Koyamada ◽  
S. Sato ◽  
S. Nakamura ◽  
H. Sotobayashi ◽  
W. Chujo
Keyword(s):  
Single Mode ◽  
Gain Spectrum ◽  
Brillouin Gain

scholarly journals Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 474
Author(s):  
Fen Xiao ◽  
Mingxing Lv ◽  
Xinwan Li
Keyword(s):  
Neural Network ◽  
Optical Fiber ◽  
Frequency Shift ◽  
Brillouin Scattering ◽  
Principal Component ◽  
Feedforward Neural Network ◽  
Gain Spectrum ◽  
Fiber Sensors ◽  
Brillouin Gain ◽  
Distributed Optical Fiber

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.

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