Classification and characterization of coexisting defects from magnetic flux leakage data using deep learning method

Abstract

Ferromagnetic materials are widely used in infrastructure, such as steam generators, storage tanks, and gas pipelines. During their service time, ferromagnetic materials are subject to deterioration and defects are prone to generate which could damage infrastructures and cause catastrophic accidents. Magnetic flux leakage (MFL) is one of the widely used nondestructive evaluation (NDE) methods to detect and characterize defects in ferromagnetic materials to ensure infrastructure safety. However, many research works have been carried out on the modeling, classification, and characterization of a single defect, while the scenario of coexisting defects is ignored. In practical field, the coexistence of surface and subsurface defects within an overlapping area can cause much earlier than expected deterioration or even penetration, the result of which is more damaging. Here, we propose a convolutional neural network (CNN) based deep learning method to differentiate between single defect and coexisting defects scenarios and estimate the defect sizes including length, width, and depth. Finite-element-method (FEM) simulation models are developed to investigate the effect of coexisting defects on the measured MFL data. The models with different defect parameters are calculated to generate 354 MFL data for the training and testing of deep learning method. The experimental results show that the classification accuracy of deep learning method is over 94% and higher than the traditional machine learning methods, and the defect size estimation errors are within 0.97 mm, 0.59 mm, and 3.67% of wall thickness, respectively, which are validated to be a good classification and characterization tool for the coexisting defects scenario.