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超声波断层扫描基于深层卷积神经网络的成像增强方法

Azamatjon Kakhramon Ugli Malikov¹, Manuel Fernando Flores Cuenca¹, Beomjin Kim¹

¹Graduate School of Mechanical Engineering, Pusan National University, Busan, 46241 Korea.

Journal of visualization

|June 26, 2023

概括

通过使用深度学习改进了核封闭板 (CLP) 的结构健康监测. 这种方法通过消除模糊区域来增强超声波断层成像,从而导致更清晰的缺陷可视化和改进的核安全.

科学领域:

材料科学材料科学材料科学
非破坏性测试不破坏性测试
核工程核工程是指核工程.

背景情况:

封闭板 (CLP) 的结构健康监测对于核电站安全至关重要.
超声波断层成像,使用像RAPID这样的方法,可以检测到隐藏的缺陷.
绵羊波测试面临挑战,因为多模式分散,复杂的模式选择.

研究的目的:

通过解决模糊问题来增强CLP的超声波断层图像.
提高CLP中缺陷检测的精度和清晰度.
开发一种具有成本效益和高效的CLP结构完整性的成像技术.

主要方法:

使用灵敏度分析来选择合适的Lamb波模式 (S0).
深度学习架构,特别是U-Net,用于图像细分.
转移学习被利用,因为U-Net模型的培训数据的可用性有限.

主要成果:

确定S0 Lamb波浪模式是CLP检查中最敏感的.
U-Net 架构成功地对实验性超声波断层图像进行了细分.
深度学习方法有效地消除了断层图像中的模糊区域,揭示了明显的缺陷边缘.

结论:

深度学习,特别是转移学习的U-Net,显著提高了CLP的超声波断层成像.
开发的方法改善了缺陷可视化,有助于更准确的结构健康评估.
这种方法为核设施中可靠和高效的非破坏性评估提供了一个有希望的解决方案.

关键词:

图像细分图像细分图像细分羔羊的灵敏度是波浪的灵敏度转移学习转移学习这就是U-Net.超声波羔羊的波浪是一种超声波.超声波断层扫描仪超声波断层扫描仪

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