使用人工智能模型的钠快堆内部结构缺陷监测系统

IF 2.6 3区 工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY
Hyungi Byun, Han Gil Lee, Beom Kyu Kim, Geun Dong Song, Bongsoo Lee
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引用次数: 0

摘要

本研究开发了一种具有人工智能模型 YOLOv7 的缺陷监测系统,该系统专门用于处理钠快堆(SFR)内部结构中超声波可视化系统的图像数据。对于钠快堆内部结构的安全而言,虽然这是一项重要的缺陷监测检查,但由于环境不可见,很难识别结构缺陷。因此,我们在本研究中应用了 YOLOv7 模型;然而,我们遇到了一些挑战,包括复杂缺陷形状的准确性降低,以及预训练期间数据增强带来的复杂性。为了解决这些问题,我们还应用了增强型超分辨率生成对抗网络来提高分辨率,并采用索贝尔噪声过滤算法来提高缺陷检测的准确性。我们还通过置信度评分对系统进行了评估。这凸显了该方法在提高缺陷检测能力方面的有效性。因此,这种缺陷监测系统的设计应能预先识别内部结构变形,并加强 SFR 安全和维护实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Defect Monitoring System of the Internal Structures of a Sodium Fast Reactor using an Artificial Intelligence Model
This study developed a defect-monitoring system with an artificial intelligence model, YOLOv7, which is tailored for processing image data from an ultrasonic visualization system within sodium fast reactor (SFR) internal structures. For the safety of SFR internal structures, although it is a crucial inspection for defect monitoring, it is difficult to identify structural defects because of the invisible environment. Therefore, we applied the YOLOv7 model in this study; however, we encountered challenges including decreased accuracy with complex defect shapes and complications from data augmentation during pre-training. To solve these problems, we additionally applied the enhanced super-resolution generative adversarial network for higher resolution and a Sobel noise-filtering algorithm to enhance the defect detection accuracy. And we evaluated our system by comparing it with a confidence score. This underscores the effectiveness of the approach in enhancing the defect detection capabilities. Therefore, this defect-monitoring system should be designed to preemptively identify internal structure deformations and enhance SFR safety and maintenance practices.
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来源期刊
Nuclear Engineering and Technology
Nuclear Engineering and Technology 工程技术-核科学技术
CiteScore
4.80
自引率
7.40%
发文量
431
审稿时长
3.5 months
期刊介绍: Nuclear Engineering and Technology (NET), an international journal of the Korean Nuclear Society (KNS), publishes peer-reviewed papers on original research, ideas and developments in all areas of the field of nuclear science and technology. NET bimonthly publishes original articles, reviews, and technical notes. The journal is listed in the Science Citation Index Expanded (SCIE) of Thomson Reuters. NET covers all fields for peaceful utilization of nuclear energy and radiation as follows: 1) Reactor Physics 2) Thermal Hydraulics 3) Nuclear Safety 4) Nuclear I&C 5) Nuclear Physics, Fusion, and Laser Technology 6) Nuclear Fuel Cycle and Radioactive Waste Management 7) Nuclear Fuel and Reactor Materials 8) Radiation Application 9) Radiation Protection 10) Nuclear Structural Analysis and Plant Management & Maintenance 11) Nuclear Policy, Economics, and Human Resource Development
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