An optical soft-sensor based shape sensing using a bio-inspired pattern recognition technique to realise fly-by-feel capability for intelligent aircraft operation

2018 ◽  
Vol 122 (1257) ◽  
pp. 1734-1752
Author(s):  
M. Basu ◽  
S. K. Ghorai
Keyword(s):  
Support Vector Regression ◽  
Time Lag ◽  
Fiber Bragg Gratings ◽  
Bragg Gratings ◽  
Support Vector ◽  
Wing Model ◽  
Sensing Systems ◽  
Structural Failures ◽  
Training Methodologies ◽  
Shape Sensing

ABSTRACTInformation regarding deformations in large and complex systems is necessary in the prediction of structural failures caused by un-natural flexural occurrences. Sensing systems which are used to predict shapes, in order to develop a global surface picture require high precision and lower time lag. In this work, a unique bio-inspired training mechanism for support vector regression is presented for shape sensing in structures mounted with Fiber Bragg Gratings. Experimental validation was carried out on a simply supported beam, loaded at different positions and an aircraft wing model for different types of bending. The resulting deflections at specified locations along the length of the beam and on both surfaces of the wing were interpreted from the wavelength shifts of the corresponding Fiber Bragg Gratings through the specially modified Support Vector Regression. The method has shown high accuracy, low computational requirements and enhanced prediction times. The proposed bio-inspired training method has also been compared with two conventional training methodologies.

Double-layer orthogonal fiber Bragg gratings flexible shape sensing technology

2021 ◽  
Vol 29 (10) ◽  
pp. 2306-2315
Author(s):  
Yong-xing GUO ◽  
◽  
Yue-hui YANG ◽  
Li XIONG ◽  
Keyword(s):  
Double Layer ◽  
Fiber Bragg Gratings ◽  
Bragg Gratings ◽  
Sensing Technology ◽  
Shape Sensing

scholarly journals 2D and 3D Shape Sensing Based on 7-Core Fiber Bragg Gratings

2020 ◽  
Vol 10 (4) ◽  
pp. 306-315
Author(s):  
Tianting Lai ◽  
Pu Cheng ◽  
Congliao Yan ◽  
Chi Li ◽  
Wenbin Hu ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Fiber Bragg Gratings ◽  
Bragg Gratings ◽  
Shape Reconstruction ◽  
Two Dimensional ◽  
3D Shape ◽  
3D Space ◽  
Core Fiber ◽  
Shape Sensing ◽  
2D And 3D

Abstract A fiber-optic shape sensing based on 7-core fiber Bragg gratings (FBGs) is proposed and experimentally demonstrated. The investigations are presented for two-dimensional and three-dimensional shape reconstruction by distinguishing bending and twisting of 7-core optical fiber with FBGs. The curvature and bending orientation can be calculated by acquiring FBG wavelengths from any two side cores among the six outer cores. And the shape sensing in three-dimensional (3D) space is computed by analytic geometry theory. The experiments corresponding of two-dimensional (2D) and 3D shape sensing are demonstrated and conducted to verify the theoretical principles. The resolution of curvature is about 0.1m−1 for 2D measuring. The error of angle in shape reconstruction is about 1.89° for 3D measuring. The proposed sensing technique based on 7-core FBGs is promising of high feasibility, stability, and repeatability, especially for the distinguishing ability on the bending orientation due to the six symmetrical cores on the cross-section.

Real-Time Surface Shape Sensing for Soft and Flexible Structures Using Fiber Bragg Gratings

2019 ◽  
Vol 4 (2) ◽  
pp. 1454-1461 ◽  
Author(s):  
Tian Le Tim Lun ◽  
Kui Wang ◽  
Justin D. L. Ho ◽  
Kit-Hang Lee ◽  
Kam Yim Sze ◽  
...  
Keyword(s):  
Real Time ◽  
Flexible Structures ◽  
Fiber Bragg Gratings ◽  
Bragg Gratings ◽  
Surface Shape ◽  
Shape Sensing

scholarly journals Large Deformation Modeling of Wing-Like Structures Based on Support Vector Regression

2020 ◽  
Vol 10 (17) ◽  
pp. 5995
Author(s):  
Chao An ◽  
Changchuan Xie ◽  
Yang Meng ◽  
Xiaofei Shi ◽  
Chao Yang
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Large Deformation ◽  
Learning Algorithm ◽  
Measurement Data ◽  
Aircraft Design ◽  
Support Vector ◽  
Design Calculation ◽  
Order Model ◽  
Wing Model

Large flexible aircrafts produce large deformation during flight, leading to obvious geometric nonlinearities. Large deformation modeling is essential for modern aircraft design. Calculation of large deformation based on a full-order model often carries an unbearable computing burden. The reduced-order model (ROM) can be efficient in calculation but requires lots of test datasets. This study investigates support vector regression (SVR) to build a regression model to calculate the static large deformation of wing-like structures. The correlation coefficient (R) and root mean square error (RMSE) are used to evaluate the performance of the regression models. In contrast to the ROM that has been proposed, the regression model based on SVR requires far fewer training cases, with almost the same accuracy in this research. Meanwhile, comparison with another prediction model built based on random forest regression (RFR) has also been reported. The results reveal that the SVR algorithm has better accuracy on this issue. Finally, ground test results of a real large flexible wing model show that the regression model proposed here reaches a good agreement with measurement data under applied load. This work illustrates that the machine learning algorithm is an efficient and accurate way to predict large deformation of aircrafts.

Multiple off-axis fiber Bragg gratings for 3D shape sensing

2018 ◽  
Vol 57 (28) ◽  
pp. 8125 ◽  
Author(s):  
Christian Waltermann ◽  
Konrad Bethmann ◽  
Alexander Doering ◽  
Yi Jiang ◽  
Anna Lena Baumann ◽  
...  
Keyword(s):  
Fiber Bragg Gratings ◽  
Bragg Gratings ◽  
3D Shape ◽  
Shape Sensing
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