Design and Measurement of a Dual FBG High-Precision Shape Sensor for Wing Shape Reconstruction

Wu, Huifeng; Liang, Lei; Wang, Hui; Dai, Shixun; Xu, Qiwei; Dong, Rui

doi:10.3390/s22010168

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…Three FBGs are operated in the same temperature field, and the Bragg wavelength (

) is shifted due to changes in strain (

) and temperature (

). The relationship between the FBG wavelength shift with strain and temperature can be expressed as [ 25 ]:

where

is the variation of the central wavelength of FBG,

is effective photo-elastic coefficient,

is the thermal expansion coefficient of the fiber, and

is the hermo optical coefficient of the fiber.…”

Section: Structural Design and Methods For Attitude Estimationmentioning

confidence: 99%

An All-Fiber Optical Sensor Combined with FBG and CFBG-FP for Attitude Estimation

Xiong

Xia

2022

Sensors

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In order to meet the needs of attitude correction of the optical fiber sensor array, an all-fiber optical sensor combined with FBG and CFBG-FP is proposed to monitor its attitude angle. The FBG sensor for pitch and roll angles detection employs a geometric model solution to shift the center wavelength of the three fiber Bragg gratings connected to it through the mass sphere, allowing for pitch and roll angle detection. Based on the magnetostrictive effect, the CFBG-FP sensor for heading angle detection employs changes of the cavity length of the chirped grating Fabry-Pérot cavity connected to its surface. The method of interpolation data processing and derivation of the attitude angle matrix is used to realize heading angle detection. The experimental results indicate that the FBG sensor for the pitch and roll angles detection has an effective detection range of 180 degrees and an angle measurement error of less than 1.5 degrees. Meanwhile, in the region beyond 10 degrees in the geomagnetic north-south direction, the angle measurement error of the CFBG-FP sensor for the heading angle detection is less than 1.3 degrees. Moreover, the impact of temperature in the system is minimized, demonstrating the validity of this combined fiber optical approach in the attitude angle measurements.

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“…Three FBGs are operated in the same temperature field, and the Bragg wavelength (

) is shifted due to changes in strain (

) and temperature (

). The relationship between the FBG wavelength shift with strain and temperature can be expressed as [ 25 ]:

where

is the variation of the central wavelength of FBG,

is effective photo-elastic coefficient,

is the thermal expansion coefficient of the fiber, and

is the hermo optical coefficient of the fiber.…”

Section: Structural Design and Methods For Attitude Estimationmentioning

confidence: 99%

An All-Fiber Optical Sensor Combined with FBG and CFBG-FP for Attitude Estimation

Xiong

Xia

2022

Sensors

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show abstract

“…Combining the characteristics of the loads (vertical forces and torques) applied to the structure, inspired by the dual FBG shape sensor designed in the literature [40], one measuring point has two gratings, as shown in Figure 1, the angles between the FBGs of the measuring point and the x-axis are α 1 and α 2 , respectively, and angles (α 1 , α 2 ) will be analyzed in the next section.…”

Section: Foss and System Simulation 21 Fbg Sensor Layoutmentioning

confidence: 99%

FOSS-Based Method for Thin-Walled Structure Deformation Perception and Shape Reconstruction

Dong

et al. 2023

Micromachines

Self Cite

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To improve the accuracy of deformation perception and shape reconstruction of flexible thin-walled structures, this paper proposes a method based on the combination of FOSS (fiber optic sensor system) and machine learning. In this method, the sample collection of strain measurement and deformation change at each measuring point of the flexible thin-walled structure was completed by ANSYS finite element analysis. The outliers were removed by the OCSVM (one-class support vector machine) model, and the unique mapping relationship between the strain value and the deformation variables (three directions of x-, y-, and z-axis) at each point was completed by a neural-network model. The test results show that the maximum error of the measuring point in the direction of the three coordinate axes: the x-axis is 2.01%, the y-axis is 29.49%, and the z-axis is 15.52%. The error of the coordinates in the y and z directions was large, and the deformation variables were small, the reconstructed shape had good consistency with the deformation state of the specimen under the existing test environment. This method provides a new idea with high accuracy for real-time monitoring and shape reconstruction of flexible thin-walled structures such as wings, helicopter blades, and solar panels.

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“…Concurrently, they pioneered the integration of the FS framework into spatial shape reconstruction, marking the inaugural application of this methodology. Subsequently, leveraging the FS framework, Wu [ 26 ] addressed the challenge of compensating for the impact of torsion or temperature on algorithmic precision by integrating double Fiber Bragg Grating (FBG) cross-laying with the FS framework’s design. In a parallel effort, Al-Ahmad [ 27 ] introduced a two-step calibration method within the FS framework to enhance accuracy, specifically focusing on torsion compensation.…”

Section: Introductionmentioning

confidence: 99%

Shape Sensing of Cantilever Column Using Hybrid Frenet–Serret Homogeneous Transformation Matrix Method

Zhang,

Li,

et al. 2024

Sensors

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The Frenet–Serret (FS) framework stands as a pivotal tool in shape sensing for various infrastructures. However, this tool suffers from accumulative errors, particularly at inflection points where the normal vector undergoes sign changes. To minimize the error, the traditional FS framework is modified by incorporating the homogeneous matrix transformation (HMT) method for segments containing inflection points. Additionally, inclination information is also used to calculate the unit tangent vector and the unit norm vector at the start point of each segment. This novel approach, termed the FS-HMT method, aims to enhance accuracy. To validate the effectiveness of the proposed method, a simulation of a cantilever column was conducted using finite element software ANSYS 19.2. The numerical results demonstrate the capability of the proposed method to accurately predict curves with inflection points, yielding a maximum error of 1.1%. Subsequently, experimental verification was performed using a 1 m long spring steel sheet, showcasing an error of 4.9%, which is notably lower than that of the traditional FS framework. Our proposed modified FS framework exhibits improved accuracy, especially in scenarios involving inflection points. These findings underscore its potential as a valuable tool for enhanced shape sensing in practical applications.

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Design and Measurement of a Dual FBG High-Precision Shape Sensor for Wing Shape Reconstruction

Cited by 9 publications

References 24 publications

An All-Fiber Optical Sensor Combined with FBG and CFBG-FP for Attitude Estimation

An All-Fiber Optical Sensor Combined with FBG and CFBG-FP for Attitude Estimation

FOSS-Based Method for Thin-Walled Structure Deformation Perception and Shape Reconstruction

Shape Sensing of Cantilever Column Using Hybrid Frenet–Serret Homogeneous Transformation Matrix Method

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