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

US11562224B2 · kind B2 · utility

Assignee

• University of Electronic Science and Technology of China · CN

Inventors

• Huijuan Wu · Sichuan, CN
• Jiping Chen · Sichuan, CN
• Xiangrong Liu · Sichuan, CN
• Yao Xiao · Beijing, CN
• Mengjiao Wang · Shanghai, CN
• Bo Tang · Lo Wu, CN
• Mingru Yang · Sichuan, CN
• Haoyu Qiu · Sichuan, CN
• Yunjiang Rao · Hangzhou City, CN

Key dates

• Filing date: Aug 8, 2018
• Grant date: Jan 24, 2023
• Expiry date: Nov 25, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/20
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

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.