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:
Feb. 21, 2023

Filed:

Dec. 24, 2019
Applicant:

Wuhan University, Hubei, CN;

Inventors:

Yigang He, Hubei, CN;

Jiajun Duan, Hubei, CN;

Hui Zhang, Hubei, CN;

Liulu He, Hubei, CN;

Assignee:

WUHAN UNIVERSITY, Hubei, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06F 1/3206 (2019.01); G06N 5/04 (2006.01); H02J 3/00 (2006.01); G06K 9/62 (2022.01); H02J 13/00 (2006.01); G01R 31/08 (2020.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G01R 31/08 (2013.01); G06F 1/3206 (2013.01); G06K 9/6232 (2013.01); G06K 9/6259 (2013.01); G06K 9/6267 (2013.01); G06K 9/6288 (2013.01); G06N 5/04 (2013.01); H02J 3/0012 (2020.01); H02J 13/00002 (2020.01);
Abstract

A method includes steps: 1) obtaining monitoring information of different monitoring points in normal state of power equipment; 2) setting faults and obtaining monitoring information of different fault types, positions, monitoring points of the equipment; 3) taking the monitoring information obtained in steps 1) to 2) as training dataset, taking the fault types and positions as labels, inputting the training dataset and the labels to deep CNN for training; 4) collecting monitoring data, performing verification and classification using step 3), obtaining probability values corresponding to each of the labels; 5) taking classification results of different labels as basic probability assignment values, with respect to a monitoring system composed of multiple sensors, taking different sensors as different evidences for decision fusion, performing fusion processing using the DS evidence theory to obtain fault diagnosis result. The invention can intelligently realize fault detection, fault type determination, and fault positioning of the power equipment.


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