TY - JOUR

T1 - Highly dense fbg temperature sensor assisted with deep learning algorithms

AU - Kokhanovskiy, Alexey

AU - Shabalov, Nikita

AU - Dostovalov, Alexandr

AU - Wolf, Alexey

N1 - Funding Information: A. Dostovalov and A.Wolf acknowledge the support of the Ministry of Science and Higher Education of the Russian Federation (14.Y26.31.0017). The work of A. Kokhanovskiy was supported by the Russian Science Foundation (Grant No. 17-72-30006-P). Funding Information: Thus, the calibration method of a highly dense FBG temperature sensor is proposed in the paper. It provides a possibility for increasing the spatial resolution of a fiberoptic Education of the Russian Federation (14.Y26.31.0017). The work of A. Kokhanovskiy was supported sensor, avoiding the complications of FBG manufacturing or of an interrogation setup. by the Russian Science Foundation (Grant No. 17-72-30006-Π). The method is an alternative to the more common approach, wherein several sparse FBGs Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/9

Y1 - 2021/9

N2 - In this paper, we demonstrate the application of deep neural networks (DNNs) for processing the reflectance spectrum from a fiberoptic temperature sensor composed of densely inscribed fiber Bragg gratings (FBG). Such sensors are commonly avoided in practice since close arrangement of short FBGs results in distortion of the spectrum caused by mutual interference between gratings. In our work the temperature sensor contained 50 FBGs with the length of 0.95 mm, edge-to-edge distance of 0.05 mm and arranged in the 1500–1600 nm spectral range. Instead of solving the direct peak detection problem for distorted signal, we applied DNNs to predict temperature distribution from entire reflectance spectrum registered by the sensor. We propose an experimental calibration setup where the dense FBG sensor is located close to an array of sparse FBG sensors. The goal of DNNs is to predict the positions of the reflectance peaks of the reference sparse FBG sensors from the reflectance spectrum of the dense FBG sensor. We show that a convolution neural network is able to predict the positions of FBG reflectance peaks of sparse sensors with mean absolute error of 7.8 pm that is slightly higher than the hardware reused interrogator equal to 5 pm. We believe that dense FBG sensors assisted with DNNs have a high potential to increase spatial resolution and also extend the length of a fiber optical sensors.

AB - In this paper, we demonstrate the application of deep neural networks (DNNs) for processing the reflectance spectrum from a fiberoptic temperature sensor composed of densely inscribed fiber Bragg gratings (FBG). Such sensors are commonly avoided in practice since close arrangement of short FBGs results in distortion of the spectrum caused by mutual interference between gratings. In our work the temperature sensor contained 50 FBGs with the length of 0.95 mm, edge-to-edge distance of 0.05 mm and arranged in the 1500–1600 nm spectral range. Instead of solving the direct peak detection problem for distorted signal, we applied DNNs to predict temperature distribution from entire reflectance spectrum registered by the sensor. We propose an experimental calibration setup where the dense FBG sensor is located close to an array of sparse FBG sensors. The goal of DNNs is to predict the positions of the reflectance peaks of the reference sparse FBG sensors from the reflectance spectrum of the dense FBG sensor. We show that a convolution neural network is able to predict the positions of FBG reflectance peaks of sparse sensors with mean absolute error of 7.8 pm that is slightly higher than the hardware reused interrogator equal to 5 pm. We believe that dense FBG sensors assisted with DNNs have a high potential to increase spatial resolution and also extend the length of a fiber optical sensors.

KW - Convolutional neural network

KW - Deep learning algorithms

KW - Distributed temperature sensor

KW - Fiber bragg grating

KW - Fully connected neural network

KW - Optical fiber sensor

KW - Temperature

KW - Algorithms

KW - Calibration

KW - Fiber Optic Technology

KW - Deep Learning

UR - http://www.scopus.com/inward/record.url?scp=85114881835&partnerID=8YFLogxK

UR - https://elibrary.ru/item.asp?id=47087350

U2 - 10.3390/s21186188

DO - 10.3390/s21186188

M3 - Article

C2 - 34577392

AN - SCOPUS:85114881835

VL - 21

JO - Sensors

JF - Sensors

SN - 1424-3210

IS - 18

M1 - 6188

ER -