NDE Based Cost-Effective Detection of Obtrusive and Coincident Defects in Pipelines Under Uncertainties

Pipeline infrastructure systems in service are aging and continue to deteriorate with the passage of time. For public safety, it is extremely important to accurately detect harmful defects in these pipelines and replace the corresponding pipe sectors before they lead to leakages. However, due to regular usages, the inner pipe surfaces can be rather non-smooth and inundated with several scratches and tiny cavities. Their minor defects within the pipeline do-not need immediate repair. As such, it will be very expensive if pipe sections just containing minor defects are replaced. In this paper, we have developed a novel method for accurate identification of large cavities and potentially harmful obtrusive defects using magnetic flux leakage (MFL) based nondestructive evaluation (NDE) technique. A substantial challenge in our set-up is the detection of possible harmful defects in the presence of several minor and tiny cavities. This translates into defect recognition under extremely noisy conditions as the MFL's intensity-based signals is heavily influenced by the presence of multiple minor defects and cavities. Based on MFL data from a wide range of feasible scenarios, we develop a robust detection algorithm that is sensitive in the detection of harmful large defects and is simultaneously also cost effective by not classifying most of the harmless cavities as harmful defects. Our detection analysis is based on nearest neighbor-based divergence measure in Wavelet transformed domain of the flux signals. We study the performance of our procedure across different regimes and obtain encouraging results.