Defect Size Quantification for Pipeline Magnetic Flux Leakage Detection System via Multilevel Knowledge-Guided Neural Network

Abstract

Defect size quantification plays a vital role in pipeline magnetic flux leakage (MFL) detection system. However, most existing methods suffer from poor applicability and low precision due to complex industrial process. Moreover, they often overlooks the valuable knowledge that may be beneficial for defect size quantification. To solve this problem, a defect size quantification method based on multi-level knowledge-guided neural network (MLKG-NN) is proposed, which effectively integrates prior knowledge and specific data. First, at the feature level, a recursive residual subnet is proposed to inject the mechanism features into the network, so that the network performance can be enhanced. Second, an experience-aided subnet is proposed to incorporate expert experience at the decision level, which supervises the network training with labels, so that the network stability can be improved. Third, at the modeling level, a cascade expression subnet based on two-point representation is first proposed in the defect size quantification area, where both the value and distribution of labels are considered to boost the network precision. These three parts are jointly trained and promote each other. Finally, extensive experiments are conducted with defects from a pipeline network in northern China and the experimental results highlight the superiority of our method.