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Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

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Published on: November 7, 2016

Highly Dense FBG Temperature Sensor Assisted with Deep Learning Algorithms.

Alexey Kokhanovskiy¹, Nikita Shabalov^1,2, Alexandr Dostovalov^1,2

¹Novosibirsk State University, 1 Pirogova Street, Novosibirsk 630090, Russia.

Sensors (Basel, Switzerland)

|September 28, 2021

Summary

This summary is machine-generated.

Deep neural networks (DNNs) process distorted spectra from dense fiber Bragg gratings (FBGs) for accurate temperature sensing. This approach overcomes limitations of traditional methods, enabling higher spatial resolution in fiber optic sensors.

Keywords:

convolutional neural network deep learning algorithms distributed temperature sensor fiber bragg grating fully connected neural network optical fiber sensor

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Area of Science:

Optical Engineering
Sensor Technology
Artificial Intelligence

Background:

Fiber Bragg gratings (FBGs) are widely used in fiber optic sensors.
Dense FBG arrangements lead to spectral distortions, limiting their practical application.
Traditional peak detection methods struggle with distorted FBG spectra.

Purpose of the Study:

To apply deep neural networks (DNNs) for processing reflectance spectra from dense FBG sensors.
To overcome spectral distortion issues in densely inscribed FBGs.
To enhance the capabilities of fiber optic temperature sensors.

Main Methods:

Utilized a fiber optic temperature sensor with 50 densely inscribed FBGs (0.95 mm length, 0.05 mm spacing).
Employed deep neural networks (DNNs), specifically a convolutional neural network (CNN), to analyze the entire reflectance spectrum.
Implemented an experimental calibration setup with sparse FBG sensors as a reference.

Main Results:

DNNs successfully predicted the positions of sparse FBG reflectance peaks from the dense FBG sensor's spectrum.
Achieved a mean absolute error of 7.8 pm in peak position prediction using the CNN.
Performance was slightly lower than a hardware interrogator (5 pm error) but demonstrated DNN feasibility.

Conclusions:

DNNs offer a viable solution for interpreting distorted spectra from dense FBG sensors.
This approach can significantly increase spatial resolution and extend the sensing length of fiber optic sensors.
Dense FBG sensors, augmented by DNNs, show high potential for advanced sensing applications.