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Insulator spontaneous explosion detection method based on deep learning model

A detection method and deep learning technology, applied in the field of fault detection of electronic components, can solve problems such as setting parameters and large errors, achieve high practicability, wide application range, and improve recognition accuracy

Pending Publication Date: 2021-04-09
XIAN UNIV OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to provide an insulator self-explosion detection method based on a deep learning model, which solves the problem that the existing insulator detection method requires artificial setting and adjustment of parameters and large errors

Method used

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  • Insulator spontaneous explosion detection method based on deep learning model
  • Insulator spontaneous explosion detection method based on deep learning model
  • Insulator spontaneous explosion detection method based on deep learning model

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Embodiment Construction

[0052] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0053]A kind of insulator self-explosion detection method based on deep learning model of the present invention, refer to figure 1 , including the following steps:

[0054] Step 1, use the drone to collect 26 insulator images of the same insulator, divide the original 26 insulator images into two parts, 22 for model training, and 4 for model testing, because the insulators collected in this example There are few images, so the selected 22 images are further expanded using data enhancement technology, and operations such as image rotation, image left-right swapping, and image zoom-in and zoom-out in the Augmentor data enhancement tool are performed according to random probabilities to obtain the expanded data Set 550 images, expanding the data set used for model training to 25 times the original;

[0055] figure 2 is the original insulator...

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Abstract

The invention discloses an insulator spontaneous explosion detection method based on a deep learning model, and the method comprises the steps: collecting an insulator image, converting the insulator image into single-channel marking images, constructing a U-Net model and a CNN model, training the U-Net model and the CNN model through a part of the single-channel marking images, improving the pixel precision of the rest of single-channel marking images through the trained U-Net model, obtaining a mask image with the optimal pixel, inputting the mask image into the trained CNN model, and if the CNN model outputs a numerical value greater than 0.5, determining that the insulator is not self-exploded; and otherwise, determining that the insulator is subjected to spontaneous explosion. When the method is used for detecting the state of the insulator, the manual workload can be effectively reduced, and the recognition efficiency and definition are improved.

Description

technical field [0001] The invention belongs to the technical field of fault detection of electronic components, and relates to a self-explosion detection method of an insulator based on a deep learning model. Background technique [0002] Insulator strings are important components in high-voltage transmission lines and play an important role in electrical insulation and mechanical support. Insulators are exposed to wildlife and meteorological conditions such as rain, wind or snow, and these components are prone to things like cracking, contamination, and even explosions. Self-explosion of insulator components will lead to serious power outages on transmission lines. For electric companies, self-explosion of insulator components is harmful and far-reaching. It is very necessary to detect the state of insulators in time to prevent self-explosion. [0003] Traditional insulator blew inspections require professionals to examine video sequences for potential defects in power tr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0004G06N3/08G06T2207/10016G06N3/045
Inventor 王倩王晔琳李俊何复兴朱龙辉李宁李贺
Owner XIAN UNIV OF TECH
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