Temperature measurement by means of fiber specklegram sensors (FSS)

Castaño, Luis F.; Gutiérrez, L.; Quíjano, Jairo; Herrera-Ramírez, Jorge; Hoyos, Alejandro; Vélez, Fredy; Aristizabal, Victor H.; Silva-Núnez, L. C. da; Gómez, Jorge

doi:10.7149/opa.51.3.50306

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…These regions of greater variation generally correspond to abrupt changes in the sequence of specklegrams, which is precisely where the models obtained with interrogation strategies based on correlation have the greatest problem. These abrupt changes between some of the specklegrams make this type of model only valid in a limited range of temperatures [2,7,14]. Otherwise, the two proposed models based on machine learning can obtain a representation that covers the entire proposed range of temperatures, which means that they may be better able to handle those abrupt changes between specklegrams.…”

Section: Resultsmentioning

confidence: 99%

“…is the wavelength, n is the refractive index of the multimode fiber. This index can be calculated, for a given temperature T, using Equation (2) [2,14].…”

Section: Methodology and Materialsmentioning

confidence: 99%

“…This speckle pattern is the result of the propagation and interference of modes along the fiber, so the pattern may have changes due to some disturbances external to the fiber. Therefore, the FSS can be used to know the magnitude of an external disturbance according to the change produced in that specklegram, hence different parameters are considered in these measurements including temperature [2], mechanical deformations like strain [3] or bending [4], etc.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor

Arango

Aristizabal

Carrasquilla

et al. 2021

J. Phys.: Conf. Ser.

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Fiber optic specklegram sensors use the modal interference pattern (or specklegram) to determine the magnitude of a disturbance. The most used interrogation methods for these sensors have focused on point measurements of intensity or correlations between specklegrams, with limitations in sensitivity and useful measurement range. To investigate alternative methods of specklegram interrogation that improve the performance of the fiber specklegram sensors, we implemented and compared two deep learning models: a classification model and a regression model. To test and train the models, we use physical-optical models and simulations by the finite element method to create a database of specklegram images, covering the temperature range between 0 °C and 100 °C. With the prediction tests, we showed that both models can cover the entire proposed temperature range and achieve an accuracy of 99.5%, for the classification model, and a mean absolute error of 2.3 °C, in the regression model. We believe that these results show that the strategies implemented can improve the metrological capabilities of this type of sensor.

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Section: Resultsmentioning

confidence: 99%

“…is the wavelength, n is the refractive index of the multimode fiber. This index can be calculated, for a given temperature T, using Equation (2) [2,14].…”

Section: Methodology and Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor

Arango

Aristizabal

Carrasquilla

et al. 2021

J. Phys.: Conf. Ser.

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show abstract

“…This careful selection enables the designing of sensing systems characterized by a linear response. [18][19][20][21][22][23]26,33,34].…”

Section: Introductionmentioning

confidence: 99%

Fiber Speckle Sensing Scheme By Optical Power Filtering: Performance Analysis For Temperature Measurements

Vélez-Hoyos,

Aristizábal-Tique,

Quijano-Pérez

et al. 2024

Preprint

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This study introduces a Fiber Specklegram Sensor (FSS) design for temperature measurement utilizing a single-multi-single mode optical fiber configuration. FSS technology offers unique advantages for potential development into portable sensing systems. This investigation explores an FSS design compatible with miniaturization by optimizing the interrogation system based on the relationship between speckle grain size and filtering fiber area. Temperature variations are detected through fluctuations in optical power at the sensor output, offering a simple and reduced interrogation scheme. The performance of the FSS is assessed through post-calibration measurements compared to a commercial Fiber Bragg Grating (FBG) sensor, demonstrating comparable results. These findings pave the way for compact and portable fiber optic temperature sensing systems leveraging FSS advantages while maintaining compatibility with established fiber optic measurements.

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Numerical study using finite element method for the thermal response of fiber specklegram sensors with changes in the length of the sensing zone

et al. 2021

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The response of fiber specklegram sensors (FSSs) is given as function of variations in the intensity distribution of the modal interference pattern or speckle pattern induced by external disturbances. In the present work, the behavior of a FSS sensing scheme under thermal perturbations is studied by means of computational simulations of the speckle patterns. These simulations are generated by applying the finite element method (FEM) to the modal interference in optical fibers as a function of the thermal disturbance and the length of the sensing zone. A correlation analysis is performed on the images generated in the simulations to evaluate the dependence between the changes in the speckle pattern grains and the intensity of the applied disturbance. The numerical simulation shows how the building characteristic of the length of sensing zone, combined with image processing, can be manipulated to control the metrological performance of the sensors.

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Temperature measurement by means of fiber specklegram sensors (FSS)

Cited by 4 publications

References 31 publications

Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor

Deep learning classification and regression models for temperature values on a simulated fibre specklegram sensor

Fiber Speckle Sensing Scheme By Optical Power Filtering: Performance Analysis For Temperature Measurements

Numerical study using finite element method for the thermal response of fiber specklegram sensors with changes in the length of the sensing zone

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