Reconstruction of 3-D pipeline corrosion defect profile from MFL signals with deep learning approach

Magnetic flux leakage (MFL) is a widely used nondestructive testing technique in pipeline inspection to detect and quantify defects. In pipeline integrity management, the reconstruction of defects from MFL signals plays a critical role in failure pressure prediction and maintenance decision-making. In current research practices, this reconstruction primarily involves the determination of defect dimensions, including length, width, and depth, collectively forming a rectangular box. However, this box-based representation potentially leads to conservative assessments of pipeline integrity. To fine-scale the reconstruction results and provide detailed defect information for the integrity assessment, a 3-D reconstruction model for pipeline corrosion defects from MFL signals is proposed. In detail, the deep neural network is established to capture the nonlinear relationship between the MFL signals and 3-D defect profiles. In contrast to the limited insights offered by the box profile, the reconstructed 3-D profile in this paper enables more detailed metal loss geometry. The experiments using field pipeline in-line inspection data demonstrate promising results on both morphology and depth prediction.