Intrusion location technology of Sagnac distributed fiber optical sensing system based on deep learning

Wu, Jinyi, Zhuo, Rusheng, Wan, Shengpeng, Xiong, Xinzhong, Xu, Xinliang, Liu, Bin, Liu, Juan, Shi, Jiulin, Sun, Jizhou, He, Xingdao and Wu, Qiang (2021) Intrusion location technology of Sagnac distributed fiber optical sensing system based on deep learning. IEEE Sensors Journal. pp. 1-8. ISSN 1530-437X (In Press)

[img]
Preview
Text
Intrusion location technology of Sagnac distributed fiber optical sensing system based on deep learning.pdf - Accepted Version

Download (2MB) | Preview
Official URL: https://doi.org/10.1109/jsen.2021.3070721

Abstract

For distributed fiber optical sensing based on Sagnac effect, the intrusion is usually located by notch frequency. However, the notch spectrum is the comprehensive result of the intrusion, so when multiple disturbances simultaneously intrude from different positions of the sensing fiber, it is impossible to establish a mathematical expression between the intrusion position and the notch frequency, this leads to the problem of multi-point intrusion localization. Therefore, in this paper, deep learning technology is used to locate multiple disturbing points in Sagnac distributed optical fiber sensing system, and the related specific technologies of deep learning appling to sagnac distributed optical fiber sensing are studied. First, according to the characteristics of the system, a network structure based on the regression probability distribution is proposed, second, a loss function is constructed. The results show that the trained model can realize the positioning of multiple and single intrusion points.

Item Type: Article
Uncontrolled Keywords: Fiber optical sensor, Sagnac interferometers, position measurement, deep learning
Subjects: F300 Physics
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 14 Apr 2021 11:33
Last Modified: 14 Apr 2021 11:45
URI: http://nrl.northumbria.ac.uk/id/eprint/45922

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics