Application of a one-dimensional convolutional neural network in defect size inversion of oil and gas pipelines

The strong non-linearity and low prediction accuracy of defect depth inversion often lead to challenges in magnetic flux leakage (MFL) detection and evaluation. If a one-dimensional convolutional neural network (1D-CNN) model could automatically extract the features of the original MFL signal and its simple and compact configuration, then real-time and low-cost hardware implementation should be achievable in the future. Therefore, in this work, a pipeline defect size inversion method based on a 1D-CNN model is proposed and the optimal network hierarchy is explored. In order to fully fuse the three-axis MFL signal and input it into the 1D-CNN for defect size inversion, three signal input strategies suitable for MFL defect detection based on the 1D-CNN are proposed and the quantitative results of the different three-axis MFL signal input strategies for an artificial defect dataset are compared and analysed. Experimental results indicate that using a lateral connection between the three-axis MFL signals can effectively achieve high-precision quantification of defect size and especially the quantification of defect depth. In addition, the comprehensive comparison and analysis with other network structures and traditional algorithms also verify that the 1D-CNN method still has advantages in accuracy and time complexity.