Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Defect recognition method based on a power equipment defect recognition learning model

A technology for defect identification and power equipment, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problems of low precision, large number of power equipment defect detection samples, and long time consumption, etc., to ensure accurate Sex and efficiency, save human resources and time management costs, and improve the effect of automation

Inactive Publication Date: 2019-06-11
SOUTHWEST JIAOTONG UNIV
View PDF4 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a defect recognition method based on a learning model for electric device defect recognition, which solves the problems of large number of samples, low precision and long time-consuming for electric device defect detection

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
  • Defect recognition method based on a power equipment defect recognition learning model
  • Defect recognition method based on a power equipment defect recognition learning model
  • Defect recognition method based on a power equipment defect recognition learning model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0034] Such as figure 1 As shown, a defect recognition method based on the learning model of electric equipment defect recognition includes the following steps:

[0035] S1. Collect image data of electrical equipment in batches through X-ray imaging equipment;

[0036] The internal structural components or state of the power equipment obtained through the principle of X-ray imaging are relatively accurate. A high-definitio...

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 defect recognition method based on a power equipment defect recognition learning model. The method is based on multi-layer label labeling and identification; According to thenovel learning model integrating feature extraction, boundary frame regression and classifier technology set, a priori probability is replaced by a clustering algorithm in the model, a training set is generated by training power equipment samples, the type and the defect of the power equipment can be detected in real time, the accuracy and the high efficiency of defect recognition are ensured, and the safe operation of the power equipment is ensured. The automation degree of power equipment state monitoring and defect recognition is improved, a power grid big data analysis and processing system is researched and developed, the power equipment defect recognition technical concept is innovated, and technical development in the field of power systems is promoted. Advanced technologies are applied to the field of power systems, manpower resources and time management cost are saved, and field development is promoted.

Description

technical field [0001] The invention relates to the technical field of electrical equipment detection, in particular to a defect identification method based on a learning model for identification of electrical equipment defects. Background technique [0002] With the interconnection of large power grids and the continuous expansion of the grid scale, the safety and stability of power equipment has attracted widespread attention. The traditional types of electrical equipment defects are judged by inspectors based on their own experience. On the one hand, with the renewal of power equipment and the continuous change of the operating environment, the types of equipment defects increase, and the corresponding detection experience is also constantly changing. On the other hand, with the extension of transmission lines and the increase of electrical equipment, the contradiction between the increase in the workload of equipment defect detection and the shortage of corresponding de...

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): G06T7/00G06K9/62G06N3/04G06N3/08
Inventor 苟先太刘琪芬郭竞邓方薛宏强张鹏举
Owner SOUTHWEST JIAOTONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com