Power equipment classification method based on deep learning under small sample

A technology of power equipment and deep learning, applied in the direction of neural learning methods, instruments, biological neural network models, etc., to achieve the effect of reducing dependence, reducing manual labor, and good classification effect

Pending Publication Date: 2019-09-24
YANGZHOU UNIV
View PDF4 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But deep learning requires a large number of lab

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Power equipment classification method based on deep learning under small sample
  • Power equipment classification method based on deep learning under small sample
  • Power equipment classification method based on deep learning under small sample

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0012] Aiming at the problems of low classification accuracy of current power equipment infrared images and high degree of manual dependence, the present invention proposes a small sample-based power equipment classification method based on deep learning; Equipment classification, the solutions to be implemented are described in detail below.

[0013] like figure 1 As shown in the figure, a deep learning-based power equipment classification method under small samples includes the following steps:

[0014] Step 1: Take an infrared image on the inspection track with an infrared thermal imager. The power equipment included in the collected infrared image includes: bushings, arresters, wall bushings, wires, cable terminals, power cables, and power capacitors , Current transformers, voltage transformers, terminal boxes, circuit breakers, discharge coils, high-voltage fuses, isolation switches, transformers, switch cabinets, screen cabinets, radiators. And rotate each image by 90 ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a power equipment classification method based on deep learning under a small sample in the field of image processing. The power equipment classification method comprises the following steps: step 1, obtaining a standardized power equipment infrared image through a substation equipment detection device; step 2, establishing an electric power equipment infrared image sample library, and making a training set, a verification set and a test set; step 3, establishing a small sample learning network, training the established convolutional neural network by using a training set of a sample library, verifying the model through a verification set, and obtaining a connection weight and a bias parameter of the network model after training; and step 4, classifying the infrared images in the test set by using the trained network model to generate a classification result of the infrared images of the power equipment, obtaining a good effect under the condition that the sample size is relatively small, and not needing a large amount of training time, and being applicable to management and control of the power equipment.

Description

technical field [0001] The invention relates to electric equipment, in particular to a method for classifying electric equipment. Background technique [0002] The infrared image of the power equipment is to detect the infrared radiation energy emitted by the power equipment and convert it into a corresponding electrical signal. After the electrical signal is processed, the thermal image of the surface of the power equipment is obtained. Infrared detection technology has the characteristics of long-distance, no contact, no sampling, no disassembly, accuracy, speed, and intuitiveness. It is widely used in the detection and diagnosis of power equipment, and is of great significance to improving the stability of power systems. But the infrared image is a kind of false color image, which reflects the level and distribution of the surface temperature of the object, and has the characteristics of concentrated intensity and low contrast. And limited by the technology of infrared i...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q50/06
CPCG06N3/08G06Q50/06G06N3/045G06F18/241
Inventor 郭志波崔正大姚新
Owner YANGZHOU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap