Design on forward modeling of RFEC inspection for cracks

Being an inverse problem of Electromagnetic fields, the quantitative inspection of pipeline cracks in Remote Field Eddy Current (RFEC) becomes an ill-posed problem for the lack of prior constraints. Here we demonstrated the significant mapping between the cracks and the features of magnetic signals through the researches on the axially symmetric defects of pipeline. A forward modeling, which can quantitatively map the pipeline defects to the features of magnetic signals, based on Back-Propagation Neural Network (BPNN) was proposed. The high approximation accuracy and good generalization ability of the forward modeling mean the effective prior knowledge and constraints for the quantitative inverse of the pipeline defects.