Positioning method for Sagnac distributed optical fiber sensing system based on convolutional neural network ensemble learning

Abstract

The invention discloses a positioning method for a Sagnac distributed optical fiber sensing system based on convolutional neural network ensemble learning. The method comprises the steps of taking a fixed interval point on a sensing optical fiber as a disturbance point, enabling the sensing system to obtain an interference signal generated by the simulation disturbance of each point, carrying outthe preprocessing of the interference signal, and enabling the preprocessed interference signal to serve as a training set and a verification set, training two convolutional neural network models fordifferent loss functions to enable the two CNN models to accurately position near-end and far-end disturbances respectively, combining training results of the two models through an ensemble learning method to obtain a disturbance position prediction model based on CNN ensemble learning, and optimizing parameters of each model through the verification set. A to-be-positioned interference signal ispre-processed as a test sample, and the to-be-positioned interference signal is tested by using the trained prediction model to obtain a disturbance position of the to-be-positioned interference signal. The method does not need signal demodulation, is low in system complexity, is not sensitive to noise, is simple in data processing method, is stable and accurate in positioning result, and can be used for the disturbance positioning of an annular or linear Sagnac distribution optical fiber sensing system.

Description

technical field [0001] The invention relates to a positioning method for a distributed optical fiber sensing system, in particular to a positioning method for a Sagnac distributed optical fiber sensing system based on convolutional neural network (CNN) integrated learning. Background technique [0002] Distributed optical fiber sensing technology has broad application prospects in the detection and location of high-pressure pipeline leaks. Among them, the Sagnac distributed optical fiber sensing system has the advantages of strong anti-interference and low light source requirements, and is one of the current research hotspots. The zero frequency method is the main positioning method of the Sagnac distributed optical fiber sensing system, but the zero frequency is often submerged in the noise, which affects the positioning accuracy. The accuracy of positioning can be improved by improving the structure of the sensing system and using phase-generated carrier homodyne demodulat...

AI Technical Summary

Problems solved by technology

Some scholars use the support vector machine (SVM) regression model to make the measured position close to the real position, but it needs to use the zero frequency method for preliminary positioning in advance, and the positioning process is complicated

The applicant once proposed a discretization positioning method based on a machine learning classification model of a Sagnac distributed optical fiber sensing system, which transformed the disturbance positioning problem into a multi-classification problem of interference signals caused by disturbances in the positions of different sensing fibers. However, this The classification method needs to collect interference signals at all disturbance locations, and loses the advantage of continuous monitoring of distributed optical fiber sensing, which has become an urgent technical problem to be solved

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