Reconstruction of 3-D pipeline defect profile based on MFL signals and hybrid neural networks
提出一种混合神经网络方法，利用磁漏信号重建管道缺陷的三维轮廓，帮助油气行业操作人员可视化地下管道缺陷并制定维护措施。
The pipelines' in-line inspection (ILI) is critical within the integrity management framework in the oil and gas industry. Furthermore, the reconstruction of defects' three-dimensional (3-D) profile using the magnetic flux leakage (MFL) signals acquired has great significance. However, most existing methods only focus on estimating defect sizes or shape parameters instead of the defect's 3-D profile. This study proposes an innovative approach for reconstructing the defect profile using a novel hybrid neural network to accurately and efficiently map three-axial MFL signals to the defects' 3-D profile. This paper utilizes the neural ordinary differential equation (ODE) as a module within the neural network architecture. The neural ODE is used to map the processed MFL signals to the spatial position of each point on the defective concave surface. Additionally, the model incorporates the Fourier integration kernel (FIK) to enhance computational efficiency. The proposed model is trained using finite element (FE) simulation data and then transferred to an experimental dataset, which addresses the challenge of limited availability of experimental data while maintaining accuracy. Furthermore, the proposed method also exhibits a high degree of accuracy in reconstructing the rotational angles of the defects. Therefore, the proposed method helps visualize defects in underground pipes via the analysis of MFL signals, facilitating operators in undertaking subsequent maintenance measures and providing a foundation for pipeline digital integrity management.