University of Electronic Science and Technology of China

Mengjiao Wang

Yunjiang Rao

Yao Xiao

Huijuan Wu

Xiangrong Liu

Jiping Chen

Haoyu Qiu

Bo Tang

Mingru Yang

A 1D-CNN-based ((one-dimensional convolutional neural network)-based) distributed optical fiber sensing signal feature learning and classification method is provided, which solves a problem that an existing distributed optical fiber sensing system has poor adaptive ability to a complex and changing environment and consumes time and effort due to adoption of manually extracted distinguishable event features. The method includes steps of: segmenting time sequences of distributed optical fiber sensing acoustic and vibration signals acquired at all spatial points, and building a typical event signal dataset; constructing a 1D-CNN model, conducting iterative update training of the network through typical event signals in a training dataset to obtain optimal network parameters, and learning and extracting 1D-CNN distinguishable features of different types of events through an optimal network to obtain typical event signal feature sets; and after training different types of classifiers through the typical event signal feature sets, screening out an optimal classifier.

1D-CNN-based distributed optical fiber sensing signal feature learning and classification method

Growing community of inventors

Sichuan, China

Mengjiao Wang