Location method of Sagnac distributed optical fiber sensing system based on CNNs ensemble learning

Abstract

Aiming at the location problem of high-pressure pipeline leakage, this paper proposes a method for predicting the disturbed position of a Sagnac distributed optical fiber sensing system based on ensemble learning of convolutional neural networks (CNNs). A Stacking ensemble method is used to combine two different CNN models to improve the stability and location accuracy of the disturbance position prediction model. By training the prediction model using the spectrum features of interference signals generated by the disturbances at partial sensing positions, accurate prediction of an arbitrary disturbance position can be realized. The disturbance position prediction was carried out numerically on a sensing fiber with an effective length of 8.5 km. Simulation results show that a mean absolute error of no more than 14.6 m and a location resolution of 10 m are achieved. The method is insensitive to noise, low in system complexity, simple in data processing and accurate in location results.