Design and Optimization of FBG Implantable Flexible Morphological Sensor to Realize the Intellisense for Displacement

The measurement accuracy of the intelligent flexible morphological sensor based on fiber Bragg grating (FBG) structure was limited in the application of geotechnical engineering and other fields. In order to improve the precision of intellisense for displacement, an FBG implantable flexible morphological sensor was designed in this study, and the classification morphological correction method based on conjugate gradient method and extreme learning machine (ELM) algorithm was proposed. This study utilized finite element simulations and experiments, in order to analyze the feasibility of the proposed method. Then, following the corrections, the results indicated that the maximum relative error percentages of the displacements at measuring points in different bending shapes were determined to be 6.39% (Type 1), 7.04% (Type 2), and 7.02% (Type 3), respectively. Therefore, it was confirmed that the proposed correction method was feasible, and could effectively improve the abilities of sensors for displacement intellisense. In this paper, the designed intelligent sensor was characterized by temperature self-compensation, bending shape self-classification, and displacement error self-correction, which could be used for real-time monitoring of deformation field in rock, subgrade, bridge, and other geotechnical engineering, presenting the vital significance and application promotion value.

[1]  Ismael Payo,et al.  Fibre Bragg grating (FBG) sensor system for highly flexible single-link robots , 2009 .

[2]  Hong-Il Kim,et al.  Measurement of strain and bending deflection of a wind turbine tower using arrayed FBG sensors , 2012 .

[3]  Nam-Sik Kim,et al.  Estimating deflection of a simple beam model using fiber optic bragg-grating sensors , 2004 .

[4]  Xiaojin Zhu,et al.  Spatial shape reconstruction using orthogonal fiber Bragg grating sensor array , 2012 .

[5]  Wei-Xin Ren,et al.  Deflection Estimation of Bending Beam Structures Using Fiber Bragg Grating Strain Sensors , 2015 .

[6]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[7]  Jian Liu,et al.  Structural Stability Monitoring of a Physical Model Test on an Underground Cavern Group during Deep Excavations Using FBG Sensors , 2015, Sensors.

[8]  Xinwu Liang,et al.  Shape Detection Algorithm for Soft Manipulator Based on Fiber Bragg Gratings , 2016, IEEE/ASME Transactions on Mechatronics.

[9]  K. Gerstle.  Advanced Mechanics of Materials , 2001 .

[10]  Adnan Kefal,et al.  Modeling of Sensor Placement Strategy for Shape Sensing and Structural Health Monitoring of a Wing-Shaped Sandwich Panel Using Inverse Finite Element Method , 2017, Sensors.

[11]  T Allsop,et al.  Arbitrary real-time three-dimensional corporal object sensing and reconstruction scheme. , 2012, Optics letters.

[12]  Qingmei Sui,et al.  Deformation reconstruction of a smart Geogrid embedded with fiber Bragg grating sensors , 2015 .

[13]  Hong-hu Zhu,et al.  Monitoring of lateral displacements of a slope using a series of special fibre Bragg grating-based in-place inclinometers , 2012 .

[14]  Ka-Wai Kwok,et al.  Bidirectional Soft Silicone Curvature Sensor Based on Off-Centered Embedded Fiber Bragg Grating , 2016, IEEE Photonics Technology Letters.

[15]  Jae-Hung Han,et al.  Displacement field estimation for a two-dimensional structure using fiber Bragg grating sensors , 2009 .

[16]  Jae-Hung Han,et al.  Shape estimation with distributed fiber Bragg grating sensors for rotating structures , 2011 .

[17]  Patrice Mégret,et al.  Fiber Bragg Grating Sensors toward Structural Health Monitoring in Composite Materials: Challenges and Solutions , 2014, Sensors.

[18]  S. Misra,et al.  Three-Dimensional Needle Shape Reconstruction Using an Array of Fiber Bragg Grating Sensors , 2014, IEEE/ASME Transactions on Mechatronics.

[19]  Wei Jin,et al.  Monitoring Internal Displacements of a Model Dam Using FBG Sensing Bars , 2010 .

[20]  Michael D. Todd,et al.  A locally exact strain-to-displacement approach for shape reconstruction of slender objects using fiber Bragg gratings , 2013, Smart Structures.

[21]  Xiaojin Zhu,et al.  Dynamic Error Analysis Method for Vibration Shape Reconstruction of Smart FBG Plate Structure , 2016 .

[22]  Shaobo Liu,et al.  Development and operation of a fiber Bragg grating based online monitoring strategy for slope deformation , 2015 .

[23]  Umesh Tiwari,et al.  Fiber grating sensors in medicine: Current and emerging applications , 2011 .

[24]  Jae-Hung Han,et al.  Estimation of dynamic structural displacements using fiber Bragg grating strain sensors , 2007 .

[25]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[26]  Mable P Fok,et al.  Dual-layer orthogonal fiber Bragg grating mesh based soft sensor for 3-dimensional shape sensing. , 2017, Optics express.

[27]  Qingmei Sui,et al.  In-situ calibrated deformation reconstruction method for fiber Bragg grating embedded smart Geogrid , 2016 .

[28]  Xiaoping Lou,et al.  Optical fiber shape sensing of polyimide skin for a flexible morphing wing. , 2017, Applied optics.