Estimation of Defect Size and Cross-Sectional Profile for the Oil and Gas Pipeline Using Visual Deep Transfer Learning Neural Network
Min Zhang, Yanbao Guo, Qiuju Xie, Yuansheng Zhang, Deguo Wang, Jinzhong Chen
Abstract
The magnetic flux leakage (MFL) defect detection of oil and gas pipelines faces two tasks, defect type identification and defect size and shape estimation. However, there are few pieces of research on defect shape estimation, especially fewer research works on defect cross-sectional profile estimation. The complex nonlinear relationship between the defect profile and the MFL signal makes the defect profile difficult to be estimated. In this article, we propose a novel visual deep transfer learning (VDTL) neural network, which not only predicts the defect size but also estimates the defect cross-sectional profile. VDTL network consists of a visual data transformation layer, a transfer learning convolutional neural network (CNN) layer, and a fully connected layer. In addition, we propose an augmentation data to figure (ADF) transformation method for one-dimensional MFL signals, and a fusion algorithm for two-dimensional radial and axial MFL images, which enriches the defect information in the images. Based on the Alexnet network, the multikernel maximum mean discrepancy (MK-MMD) transfer learning algorithm is introduced to improve the accuracy. Experiments are carried out on the data collected in the laboratory and on the data simulated by the finite element method. The results show that the prediction errors for defect length, depth, and defect cross-sectional profile are 0.67 mm, 0.97%, and 2.67%, which are the smallest among the other methods. The research provides a theoretical basis for accurate defect prediction and the safe maintenance of oil and gas pipelines.