Defects Size Estimation in MFL Inspection of Pipeline Based on Artificial Neural Network

Magnetic Flux Leakage (MFL) testing is an important nondestructive testing technique. Safety performance assessment of pipelines is a key aspect in practice. The inversion of defect dimensions corresponding to the characteristics of pipeline leakage signals is a hot and difficult problem in the field of pipeline defect detection. In this paper, a novel algorithm for quantitative identification of defect leakage magnetic signals is proposed. Firstly, the original leakage data is denoised and the signal is processed to better reflect the characteristics of the defect signal. Secondly, the efficiency and accuracy of defect size quantification are improved by training SSA_BP neural network to estimate defect length and width. It can handle both quantitative and qualitative information, coordinate various input information relationships, and has strong robustness and fault tolerance. In addition, two neural networks are combined with the output of the length and width to estimate the depth indirectly. The feasibility of the method is demonstrated by a large amount of simulation and experimental data to serve as support. The results show that the quantification method proposed in this paper is more accurate in estimating the defect size. This will provide accurate defect size data support for pipeline structural integrity evaluation.