Data Recovery of Magnetic Flux Leakage Data Gaps Using Multifeature Conditional Risk

Abstract

Safe transmission of oil pipelines is one of the guarantees of national defense and environmental protection. Magnetic flux leakage (MFL) testing is critical to the safety inspection of in-service pipelines. In the detection process, the incompleteness of MFL data affects defect location and inversion severely. This article proposes an MFL data recovery method based on multifeature condition risk, which can effectively handle the block data gap problem. First, a preprocessing mechanism is proposed to determine defect boundaries and uniformly interpolate the raw data automatically. Second, a multifeature extraction method of MFL defect data is proposed, which takes full advantage of the complete data and reduces the impact of data gaps. Third, a novel data reconstruction method based on feature conditional risk is proposed, where prior information of MFL data is regressed, relying on the regression coefficient calculated by dynamic programming. Finally, comparison experiments on different sizes of data gaps, varying robustness, and average running time are conducted, respectively. The MFL data are derived from actual measurements. The results indicate that the proposed method is more robust, more efficient, and faster. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the problem of data gaps in magnetic flux leakage (MFL) data in actual pipeline safety transmission measurements, which seriously affects defect inversion and assessment. In this article, in terms of the intrinsic characteristics for MFL data, we propose a new and effective approach by extracting the multifeature of defects and using conditional risk to recovery data gaps. Our method takes advantage of the no-missing part of MFL data and prior samples, which are obtained from practical measurements. The experiment results show that our proposed method is more advantageous than the comparison methods, and therefore, our proposed method has strong practical value. Our future work will aim at improving the versatility of the proposed method.