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. 27, 2024

Filed:

Dec. 17, 2020
Applicant:

Wuhan University, Hubei, CN;

Inventors:

Yigang He, Hubei, CN;

Jiajun Duan, Hubei, CN;

Xiaoxin Wu, Hubei, CN;

Liulu He, Hubei, CN;

Hui Zhang, Hubei, CN;

Guolong Shi, Hubei, CN;

Assignee:

WUHAN UNIVERSITY, Hubei, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01M 15/02 (2006.01); G06F 30/27 (2020.01); G01M 15/05 (2006.01); G01M 15/11 (2006.01); G06N 3/08 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G01M 15/02 (2013.01); G01M 15/05 (2013.01); G01M 15/11 (2013.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 30/27 (2020.01); G06N 3/08 (2013.01);
Abstract

A method and a system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces are disclosed, which belong to the field of fault diagnosis of power equipment. The method includes: test samples containing different locations, types and severity levels of fault of power equipment are acquired to form a real physical space; a virtual mirror space is acquired by simulation according to a simulation model of the equipment to be diagnosed; the training set in the real physical space is spatially integrated with the sample set in the virtual mirror space to obtain a training sample set in the twin spaces; the training sample set in the twin spaces serves as the supplement to the training set in the real physical space, and the fault type and fault location serve as diagnostic labels to be input to the deep neural network for training.


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