Machine learning approach for simultaneous measurement of strain and temperature using FBG sensor

Dey, Koustav; Vangety, Nikhil; Roy, Sourabh

doi:10.1016/j.sna.2021.113254

Cited by 32 publications

(11 citation statements)

References 24 publications

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“…To address this issue, machine learning (ML) techniques have emerged as a promising alternative for optimizing waveguide designs. In fact, ML techniques have been successfully applied to other photonic applications such as sensors, 15,16 the design of optical couplers, 17 microresonators, 18 hollow-core anti-resonant fibers, 19,20 prediction of the chromatic dispersion of PCFs, [21][22][23][24] cross-layer optimization of software-defined networks, 25 quality of transmission estimation, 25 design of nano-photonic structures, 26 and prediction of nonlinear phenomena in optical fibers. [27][28][29] For instance, Rodrigues-Esquerre et al 21 reported a multilayer perceptron (MLP) artificial neuronal network (ANN) to test and predict the chromatic dispersion of PCFs.…”

Section: Introductionmentioning

confidence: 99%

Design of porous-core photonic crystal fiber based on machine learning approach

Soto-Perdomo,

Reyes-Vera,

Montoya-Cardona

et al. 2024

Opt. Eng.

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The traditional numerical analysis in waveguide design can be time-consuming and inefficient. This is even more prominent in the THz region and with complex shapes and materials. As an alternative to overcome these drawbacks, we propose a machine learning (ML) approach to design porous-core photonic crystal fibers (PCFs) for the THz band. This method is based on an artificial neural network (ANN) model trained to predict key parameters such as the effective refractive index, effective area, dispersion, and loss values with accuracy and speed. In that sense, the network was trained to perform multiple-output regression of the above parameters. The training data for this model comes from numerical calculations that use the finite element method (FEM) to simulate and evaluate analytical expressions. Our results demonstrate the ML model's ability to capture the complex and nonlinear relationships between the input and output parameters and accurately predict the behavior of the THz PCF. Moreover, the proposed model has an inference time of ∼0.03584 s for a batch of 32 data sets, which substantially outperforms typical calculation times needed in FEM simulations for THz waveguide design. These results show that this approach is efficient and effective and has the potential to significantly accelerate the design process of PCFs for THz applications.

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Section: Introductionmentioning

confidence: 99%

Design of porous-core photonic crystal fiber based on machine learning approach

Soto-Perdomo,

Reyes-Vera,

Montoya-Cardona

et al. 2024

Opt. Eng.

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“…The wavelength of transmission and reflection of the reflected signal through the grating provides inseparable information on the influence of temperature and the effect of the strain. In separating the effects of the two solutions, the second FBG isolated from the strain is used as a reference in temperature measurements while a single FBG is left to measure the temperature and strain quantities [9], [10]. Several solutions have been given to separate the effects of strain and temperature, such as the use of FBG with different ion doping [11], slanted grating FBG, and interrogation.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of TOPAS chirped fiber Bragg grating sensor with Tanh and Gaussian apodization

Azhar

Saktioto

Marwin

2022

IJEECS

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<p>In this work we <span>carried out a numerical simulation with software Optigrating for Apodization chirped fiber Bragg grating (CFBG) with TOPAS material to improve sensitivity sensor, it was found that CFBG with a grating length of 50 mm has advantages in terms of ripple factor, side lobe left (SLL), and side lobe right (SLR) with values of -0,998 and -10,5264 dB, respectively. While the 10 mm CFBG has a narrower full-width half maximum (FWHM) with a value of 0.4528 nm. Tanh and Gaussian apodization were arranged in the CFBG design, it was found that the Tanh linear-CFBG had a narrow FWHM but for the ripple factor and the main lobe and side lobes were not good enough compared to the Tanh Cubicroot-CFBG, and the same pattern was also obtained in the Gaussian apodization. The narrow FWHM indicates the accuracy in detecting temperature, as well as the suppression of SLL and SLR. for the effect of apodization on CFBG it was found that The Tanh Linear-CFBG design with TOPAS material has the highest sensitivity which is -51.76 pm/oC compared to other designs.</span></p>

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“…In these cases strain compensation is necessary for temperature measurements. There have been several techniques developed for intrinsic strain compensation [4][5][6][7][8] . Zhu et al [4] have demonstrated a method using dualwavelength gratings to simultaneously measure temperature and strain.…”

Section: Introductionmentioning

confidence: 99%

Fiber Bragg grating temperature sensor for marine application

Wang¹,

Sun²,

Li³

et al. 2022

Advanced Sensor Systems and Applications XII

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Fiber Bragg grating (FBG) sensor for marine exploration has gradually attracted people's attention because of its natural characteristics of low investment and high expansion capability. However, it is well known that FBG is not only sensitive to temperature, but also affected by strain, pressure, vibration, refractive index etc. The cross-sensitivity of FBG results in large measurement error for fiber temperature measurement. As an important error source of optical fiber temperature sensing, strain has attracted much attention of researchers. In order to eliminate the interference of strain on FBG temperature measurement, in this paper, we propose a FBG sensor for ocean applications. Two fiber Bragg gratings with different central wavelengths are cascaded to realize the decoupling of temperature and strain sensing. Experimental results show that the temperature sensitivity of the proposed sensor can reach to 9.49 pm/°C in the range from -5°C to 35°C.

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Machine learning approach for simultaneous measurement of strain and temperature using FBG sensor

Cited by 32 publications

References 24 publications

Design of porous-core photonic crystal fiber based on machine learning approach

Design of porous-core photonic crystal fiber based on machine learning approach

Performance comparison of TOPAS chirped fiber Bragg grating sensor with Tanh and Gaussian apodization

Fiber Bragg grating temperature sensor for marine application

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