功能补偿和网络重建成像,在圆柱形波导中使用高阶螺旋模式
Zhao Wang1, Xiao Ying1, Junkai Tong1
1State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; International Institute for Innovative Design and Intelligent Manufacturing of Tianjin University-Zhejiang, Shaoxing 312000, China.
Ultrasonics
|March 18, 2025
概括
这项研究引入了一种使用高阶螺旋导波的深度学习方法,用于精确检测管道缺陷. 这种方法显著提高了墙壁损失估计的准确性和成像分辨率,即使数据有限.
科学领域:
- 非破坏性测试是指非破坏性测试.
- 超声波引导波技术的超声波技术是指导波技术的.
- 深度学习应用程序深度学习应用程序
背景情况:
- 管道完整性对于石油和天然气运输至关重要.
- 超声波导向波 (UGW) 是有效的缺陷检测.
- 在有限的视野中成像弱缺陷存在挑战.
研究的目的:
- 开发一种高分辨率的管道缺陷成像方法.
- 为了解决当前UGW逆转技术的局限性.
- 为了提高缺陷检测的准确性和稳定性.
主要方法:
- 一种循序渐进的反转方法,结合特征补偿和深度学习网络重建.
- 使用高级螺旋导向波用于扩展成像.
- 期货模型建立的有限差异方法.
- 皮尔森相关系数和壁损失估计准确度作为关键指标.
主要成果:
- 在测试样本上获得0.9669的相关系数和96.65%的最大壁损失估计准确度.
- 在不同的高斯噪声条件下 (5 dB,3 dB) 证明了强大的成像性能.
- 实验室实验证实了该方法的实际可行性.
结论:
- 拟议的深度学习方法增强了基于UGW的管道缺陷成像.
- 高级螺旋波可以改善成像视图和分辨率.
- 该方法显示了管道和类似结构的非破坏性测试的巨大潜力.
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