A Target-Focusing Optimization Method for 3-D Profile Reconstruction of Defects Using MFL Measurements

Difei Hou, Senxiang Lu, Guangmo Yi, Junxiang Qiu, Jinhai Liu

Abstract: Magnetic flux leakage (MFL) testing is a nondestructive testing method widely used in metal defect detection. Using measurements to accurately reconstruct the defect profile is one of the urgent problems in the field of MFL detection. The existing defect profile inversion algorithm can only reconstruct the 2-D profile, so it is difficult to detect the actual defects in metal materials. We propose a “target focusing” optimization method to address the problem of 3-D defect profile reconstruction. During the iterations, we focus the objective function solely on the sensor data that is strongly correlated with a particular depth point and adapt the error calculation method iteratively to accommodate small deviations. Additionally, we introduce a modified beetle swarm optimization (MBSO) algorithm with a learning component. Those improvements can overcome two major shortcomings: poor global search ability and slow convergence rate. Through the comparative experiment on the pipeline loop measurement platform, it is verified that the proposed method is far superior to the traditional optimization algorithm in terms of the convergence ability and the reconstruction accuracy of the defect profile.