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1. CN111932493 - Power distribution network partial discharge ultrasonic detection method and system

Office
China
Application Number 202010596194.1
Application Date 28.06.2020
Publication Number 111932493
Publication Date 13.11.2020
Publication Kind A
IPC
G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architecture, e.g. interconnection topology
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
CPC
G06T 7/0006
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
0002Inspection of images, e.g. flaw detection
0004Industrial image inspection
0006using a design-rule based approach
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06T 2207/10132
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
2207Indexing scheme for image analysis or image enhancement
10Image acquisition modality
10132Ultrasound image
G06N 3/084
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
084Back-propagation
G06T 7/0004
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
0002Inspection of images, e.g. flaw detection
0004Industrial image inspection
Applicants BEIJING GUOWANG FUDA SCIENCE AND TECHNOLOGY DEVELOPMENT CO., LTD.
北京国网富达科技发展有限责任公司
Inventors ZHANG TAOYUN
张涛允
XIONG PENG
熊鹏
QIN YUANXUN
秦源汛
ZHANG GUANGDONG
张广东
HE HONGTAI
何红太
ZHANG YUGANG
张玉刚
GUI FEIFEI
桂菲菲
BAI WENYUAN
白文远
WANG JIN
王津
XUE LING
薛玲
ZHANG FAGANG
张发刚
LIU KANG
刘康
HE WEIFENG
何卫锋
HUANG ZHIYONG
黄志勇
Agents 北京高沃律师事务所 11569
Title
(EN) Power distribution network partial discharge ultrasonic detection method and system
(ZH) 一种配电网局部放电超声波检测方法及系统
Abstract
(EN) The invention relates to a power distribution network partial discharge ultrasonic detection method and system based on deep learning. The method comprises the steps of training a neural network model; converting the ultrasonic signal of the partial discharge defect of the to-be-detected power distribution network equipment into Mel Frequency Cepstral data; inputting the Mel Frequency Cepstral data into a periodic neural network layer for learning to obtain a first feature; inputting the image of the partial discharge defect of the to-be-tested power distribution network equipment into the convolutional neural network layer for learning to obtain a second feature; linearly splicing the first feature and the second feature to obtain a third feature; and inputting the third feature into themulti-layer full connection layer to obtain a detection result of the to-be-detected power distribution network equipment. Compared with the existing manual detection, the detection method and systemprovided by the invention are more efficient and accurate.
(ZH) 本发明涉及一种基于深度学习的配电网局部放电超声波检测方法及系统,方法包括:训练神经网络模型;将待测配电网设备的局部放电缺陷的超声波信号转换成梅氏倒频谱数据;将梅氏倒频谱数据输入周期神经网络层进行学习得到第一特征;将待测配电网设备的局部放电缺陷的图像输入卷积神经网络层进行学习得到第二特征;将第一特征和第二特征进行线性拼接后得到第三特征;将第三特征输入多层全连接层,得到待测配电网设备的检测结果。本发明提出的检测方法及系统相对于现有的人工检测更高效、更准确。
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