A Pipeline Defect Inversion Method With Erratic MFL Signals Based on Cascading Abstract Features

Abstract

Defect inversion, as a key step in magnetic flux leakage (MFL) inspection widely used in nondestructive testing (NDT) systems, is critical to quantitative analysis of pipeline risk level. However, the conventional methods are difficult to achieve desirable results with complex and changeable MFL signals, mainly because the defect inversion procedure relies on prior knowledge and the features of MFL signals are not fully mined. To resolve the above problem, a novel direct inversion method based on cascading abstract features is proposed in this article, capable of handling feature extraction and defect size estimation problems effectively under complicated conditions. First, an adaptive abstract defect feature extraction network with stacked multipath denoising sparse autoencoder (sMPDS-AE) is constructed, and several extra feature transmitting channels are created, so that abstract features can be transferred across multiple layers. Second, multifeature fusion and sparsity rules are designed to strengthen the sMPDS-AE model and unify abstract feature dimensions. Third, a novel ensemble-backed predictor is developed to study the complex nonlinear relationship between abstract features and defect sizes. With above-mentioned structure, the proposed method can mining out abstract features of pipeline defects from MFL signals adequately and distinguish defects with different sizes more accurately. Finally, several comparison experiments are conducted on a pipeline network with MFL signals. Experimental results and comprehensive analysis with other mainstream inversion methods validate the superiority of this method.