电子测量与仪器学报

陈世利,吕玲玲,童君开,刘洋.基于物理信息嵌入式神经网络的管壁导波成像[J].电子测量与仪器学报,2023,37(8):136-145

基于物理信息嵌入式神经网络的管壁导波成像

Guided wave imaging of pipe wall based on physics embedded neural network

DOI：

英文关键词:pipeline nondestructive testing ultrasonic guided wave neural network corrosion

基金项目:国家自然科学基金(61773283)项目资助

作者	单位
陈世利	1.天津大学精密测试技术及仪器国家重点实验室
吕玲玲	1.天津大学精密测试技术及仪器国家重点实验室
童君开	1.天津大学精密测试技术及仪器国家重点实验室
刘洋	1.天津大学精密测试技术及仪器国家重点实验室

Author	Institution
Chen Shili	1.State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University
Lyu Lingling	1.State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University
Tong Junkai	1.State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University
Liu Yang	1.State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University

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中文摘要:

为了实现管壁腐蚀缺陷的定量化成像,提出了一种基于物理信息嵌入式卷积神经网络的成像算法,从超声导波信号重建管壁厚度。首先推导了超声导波在管壁上传播的二维声波模型,通过矩阵 LU 分解求解频域波动方程,可实现从管壁导波速度图到声场信号的正演;其次搭建了物理信息嵌入式卷积神经网络,包含 3 个迭代层,每个迭代层由正演模型和残差反演子网络组成;生成包含随机腐蚀缺陷的管道仿真数据集,搭建网络进行训练和反演,训练集、验证集和测试集的成像结果的平均 Pearson 相关系数分别为 94. 91%、86. 47%和 87. 37%,缺陷图像一致度高;搭建了实验系统,在加工有不规则阶梯缺陷的管道上采集导波信号进行反演,成像结果良好,厚度图的均方误差为 0. 005 7。算法将物理模型与神经网络结合在一起,实现了从导波信号到管道厚度图的高精度成像。

英文摘要:

In order to realize quantitative imaging of pipe wall corrosion defects, an imaging algorithm based on physics embedded convolution neural network is proposed to reconstruct pipe wall thickness from ultrasonic guided wave signals. Firstly, the twodimensional acoustic wave model of ultrasonic guided wave propagation on the pipe wall is derived. The wave equation in frequency domain can be solved by matrix LU decomposition to realize the forward modeling from the pipe wall guided wave velocity diagram to the acoustic field signal. Secondly, the physics embedded convolution neural network is built, including three iterative layers, each of which is composed of forward model and residual inversion subnetwork. The pipeline simulation data set containing random corrosion defects is generated, and the network is built for training and inversion. The average Pearson correlation coefficients of the imaging results of the training set, verification set and test set are 94. 91%, 86. 47% and 87. 37% respectively, and the defect image consistency is high. The experimental system is built, and the guided wave signal is collected on the pipe with irregular step defects for inversion. The imaging results is remarkable, with a mean square error of 0. 005 7 for the thickness map. The algorithm combines the physical model with neural network to achieve high-precision imaging from guided wave signal to pipeline thickness map.

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