Fast reconstruction of 3-D defect profile from MFL signals using key physics-based parameters and SVM

Fast reconstruction of three-dimensional (3-D) defect profile from three-axis magnetic flux leakage (MFL) signals is important to the pipeline inline inspection (ILI) in the oil and gas industry. Traditional methods require the processing of a large amount of raw input and output data, which poses significant challenges in balancing the inspection efficiency, i.e. sensing and data processing speed, and the ILI accuracy and robustness. Here, a novel fast reconstruction framework combining key physics-based parameters and data-driven machine learning algorithms is proposed. Geometric parameters based rational Sexier curve (RBC) model is proposed to generate the 3-D defect profile, while local and global feature parameters are determined using a nonlinear least square (NLS) approach from the three-axis MFL signals. These physics-based geometric and feature parameters are then correlated through a least-square support vector machine (LS-SVM). Meanwhile, a pipeline inspection gauge (PIG) is developed to measure the three-axis MFL signals for evaluating the reconstruction performance through field testing. Both simulation and experimental results demonstrate that the proposed method's accuracy, robustness and computation speed have been improved significantly comparing with other existing methods.