The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 14, 2020

Filed:

Nov. 14, 2017
Applicant:

University of Electronic Science and Technology of China, Chengdu, Sichuan, CN;

Inventors:

Huijuan Wu, Sichuan, CN;

Wei Zhang, Sichuan, CN;

Xiangrong Liu, Sichuan, CN;

Yunjiang Rao, Sichuan, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01V 1/28 (2006.01); G01V 1/20 (2006.01); G06F 17/12 (2006.01); G01H 9/00 (2006.01); G06N 7/00 (2006.01); G01V 1/18 (2006.01); G01V 1/22 (2006.01); G06N 5/04 (2006.01); H04B 10/25 (2013.01);
U.S. Cl.
CPC ...
G01H 9/004 (2013.01); G01V 1/18 (2013.01); G01V 1/288 (2013.01); G06N 7/005 (2013.01); G01V 1/208 (2013.01); G01V 1/226 (2013.01); G06F 17/12 (2013.01); G06N 5/04 (2013.01); H04B 10/25 (2013.01);
Abstract

A distributed optical fiber sensing signal processing method for safety monitoring of underground pipe network, which belongs to infrastructure safety monitoring field, which is aimed to improve the intelligent ability of detection and identification of the existing distributed optical fiber sound/vibration sensing system under complex application conditions. The present invention utilizes the distributed optical fiber sound/vibration sensing system to pick up the sound or vibration signal of the whole line along the detection cable; and the customized short time feature and long time feature are respectively extracted from the relative quantity of the sound or the vibration signal at each spatial point in the whole monitoring range. The Bayesian identification and classification network at each spatial point is constructed and trained based on the prior knowledge of the collected signal features and their different background noises.


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