Anomaly Detection of Complex MFL Measurements Using Low-Rank Recovery in Pipeline Transportation Inspection

Abstract

Pipeline transportation is the most economic and reasonable transport means of petroleum and natural gas. Influenced by complex corrosion environment, it is challenging to detect anomalies accurately with precise boundaries. Inspired by the idea of low-rank (LR) recovery, a novel anomaly detection model with a spatial–temporal regularization (STLR) is proposed to distinguish anomalies and background from magnetic flux leakage (MFL) measurements. By adding the STLR, the model enlarges the differences between the two parts and improves the effectiveness of anomaly detection. Then, an anomaly detection framework is proposed, where the raw MFL measurements are first transformed including profile transformation and filter transformation to prepare the measurements with high saliency. Second, a measurement fusion method is introduced to divide the transformed measurements into multiple blocks and fuse all detection results together. In experiments, the simulated MFL measurements generated by the magnetic dipole model are used to verify the method and obtain optimal parameters, and the real MFL measurements collected from experimental pipelines and in-service pipelines are evaluated for comparisons with other baseline methods.