A Kriging Based Fast and Efficient Method for Defect Detection in Massive Pipelines Using Magnetic Flux Leakages

Abstract

Abstract Systems in service continue to degrade with passage of time. Pipelines are among the most common systems that wear away with usage. For public safety it is of utmost importance to monitor pipelines and detect new defects within the pipelines. Magnetic flux leakage (MFL) testing is a widely used nondestructive evaluation (NDE) technique for defect detections within the pipelines, particularly those composed of ferromagnetic materials. Pipeline inspection gauge (PIG) procedure based on line-scans or 2D-scans can collect accurate MFL readings for defect detection. However, in real world applications involving large pipe-sectors such extensive scanning techniques are extremely time consuming and costly. In this paper, we develop a fast and cheap methodology that does not need MFL readings at all the points used in traditional PIG procedures but conducts defect detection with similar accuracy. We consider an under-sampling based scheme that collects MFL at uniformly chosen random scan-points over large lattices instead of extensive PIG scans over all lattice points. Based on readings for the chosen random scan points, we use Kriging to reconstruct MFL readings over the entire pipe-sectors. Thereafter, we use thresholding-based segmentation on the reconstructed data for detecting defective areas. We demonstrate the applicability of our methodology on synthetic data generated using popular finite element models as well as on MFL data collected via laboratory experiments. In these experiments spanning a wide range of defect types, our proposed novel MFL based NDE methodology is witnessed to have operating characteristics within the acceptable threshold of PIG based traditional methods and thus provide an extremely cost-effective, fast procedure with competing error rates that can be successfully used for scanning massive pipeline sectors.