scholarly journals Static pure strain sensing using dual–comb spectroscopy with FBG sensors

2019 ◽  
Vol 27 (23) ◽  
pp. 34269
Author(s):  
Ruixue Zhang ◽  
Zebin Zhu ◽  
Guanhao Wu
Keyword(s):  
Strain Sensing ◽  
Pure Strain ◽  
Fbg Sensors

Strain Sensing Properties of Grating Ends Packaged FBG Sensors

2016 ◽  
Vol 45 (2) ◽  
pp. 206004
Author(s):  
吴俊 WU Jun ◽  
陈伟民 CHEN Weimin ◽  
余葵 YU Kui ◽  
马希钦 MA Xiqin ◽  
舒岳阶 SHU Yuejie
Keyword(s):  
Strain Sensing ◽  
Sensing Properties ◽  
Fbg Sensors

scholarly journals Strengthening and Strain Sensing of Rectangular Concrete Beam Using Composites and Fbg Sensors

1999 ◽  
Vol 8 (6) ◽  
pp. 096369359900800 ◽  
Author(s):  
Kin-tak Lau ◽  
Li-min Zhou ◽  
Li Ye
Keyword(s):  
Concrete Beam ◽  
Concrete Beams ◽  
High Strain ◽  
Concrete Surface ◽  
Surface Strain ◽  
Woven Composites ◽  
Strain Sensing ◽  
Three Point Bending ◽  
Bending Load ◽  
Fbg Sensors

In this paper, a state-of-art report of an experimental investigation on the mechanical properties of the laboratory size notched-concrete beams strengthened by using fibre woven composites is presented. Fibre-optic Bragg grating (FBG) sensors have been adhered on the concrete surface before laying up the composites to monitor the strain changes at the interface when the concrete beam was subjected to three-point bending load after strengthening. The electrical strain gauges were also used to measure the surface strain of the composites and compare the results from the internal sensors. The results show that the overall flexural strengths of the strengthened specimens are increased compared with its un-strengthened status. Concrete and bonding failures were observed when the thick reinforcement was used. In addition, the results obtained from the sensor reveal that the strain at the interface of bond was higher than that measured on the surface of the composite reinforcement. High strain was measured from the sensor when debond at the interface occurred.

scholarly journals Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1070 ◽  
Author(s):  
Yibeltal Chanie Manie ◽  
Jyun-Wei Li ◽  
Peng-Chun Peng ◽  
Run-Kai Shiu ◽  
Ya-Yu Chen ◽  
...  
Keyword(s):  
Sensor Network ◽  
Measurement Accuracy ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Sensor System ◽  
Strain Sensing ◽  
Fbg Sensor ◽  
Signal Measurement ◽  
Fbg Sensors ◽  
Long Short Term Memory

In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.

Influence of bond layer characteristics on strain sensing properties of FBG sensors

2011 ◽  
Vol 19 (12) ◽  
pp. 2941-2946
Author(s):  
吴俊 WU Jun ◽  
陈伟民 CHEN Wei-min ◽  
章鹏 ZHANG Peng ◽  
刘立 LIU Li ◽  
刘浩 LIU Hao
Keyword(s):  
Strain Sensing ◽  
Sensing Properties ◽  
Bond Layer ◽  
Fbg Sensors

scholarly journals Distributed strain sensing with high spatial resolution by using FBG sensors

2007 ◽  
Vol 35 (Supplement) ◽  
pp. 158-159
Author(s):  
Hideaki Murayama ◽  
Kazuro Kageyama ◽  
Hirotaka Igawa
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Strain Sensing ◽  
Fbg Sensors ◽  
Distributed Strain
Sign in / Sign up

Export Citation Format

Share Document