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Nuclear reactor internal component surface roughness assessment method based on convolutional neural network

A convolutional neural network and surface roughness technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problem of high manpower investment, inability to guarantee the real-time and continuity requirements of material surface roughness, and low efficiency and other issues, to achieve the effect of improving efficiency, significant social and economic benefits, and avoiding personal safety

Inactive Publication Date: 2018-11-02
SOUTHWEST JIAOTONG UNIV +1
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Problems solved by technology

[0010] Aiming at the above-mentioned deficiencies in the prior art, the present invention provides a method for evaluating the surface roughness of nuclear reactor internal components based on a convolutional neural network that saves manpower input, high efficiency, high real-time performance and continuity, and solves the problems of the prior art. Existing manual detection and evaluation lead to large manpower input and low efficiency, and cannot guarantee the real-time and continuous requirements of material surface roughness detection and evaluation

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  • Nuclear reactor internal component surface roughness assessment method based on convolutional neural network
  • Nuclear reactor internal component surface roughness assessment method based on convolutional neural network
  • Nuclear reactor internal component surface roughness assessment method based on convolutional neural network

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[0062] 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.

[0063] In an embodiment of the present invention, a method for evaluating the surface roughness of nuclear reactor internals based on a convolutional neural network, such as figure 1 shown, including the following steps:

[0064] S1: Collect video data on the surface of nuclear reactor internals through the image acquisition module;

[0065] S2: Input the video data into the monitoring and early warning module, and take sc...

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Abstract

The invention discloses a nuclear reactor internal component surface roughness assessment method based on a convolutional neural network. The method comprises the following steps of S1, collecting video data; S2, obtaining image data; S3, dividing the image data into a training data set and a test data set; S4, inputting the training data set into the convolutional neural network for training, obtaining a feature identification model and outputting initial identification features; S5, classifying the initial identification features according to roughness, thereby obtaining roughness levels; S6, inputting the test data set into the feature identification model for test, and outputting secondary identification features; S7, judging whether the secondary identification features are consistentwith the roughness levels or not; and S8, displaying the roughness levels on a human-computer interaction interface. According to the method, the problem that in the prior art, the manpower investment is high and the efficiency is low due to artificial detection and assessment, and the material surface roughness detection and assessment timeliness and continuity requirements cannot be ensured issolved.

Description

technical field [0001] The invention relates to the technical field of the nuclear industry, in particular to a method for evaluating the surface roughness of nuclear reactor internal components based on a convolutional neural network. Background technique [0002] Before 2020, my country will build another 32 million-kilowatt-class nuclear power units, including those already built in Qinshan, Daya Bay, and Ling'ao, with a total installed capacity of more than 40 million kilowatts. Regular inspection and maintenance of in-service equipment in high-radiation underwater environment of nuclear power plants is an important guarantee for the safe operation of in-service nuclear power plants, and it is also a dangerous, hard and time-consuming job, which needs to solve the problems of high radiation dose and underwater operation feasibility. [0003] Judging from the current development situation in our country, to improve the safety and reliability of nuclear implementation, red...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V20/41G06N3/045
Inventor 高宏力洪鑫孙弋宋虹亮蔡璨羽由智超张永平高照兵汪洋金立天
Owner SOUTHWEST JIAOTONG UNIV
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