Deep Learning for Magnetic Flux Leakage Detection and Evaluation of Oil & Gas Pipelines: A Review

Songling Huang, Lisha Peng (), Hongyu Sun and Shisong Li
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Songling Huang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Lisha Peng: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Hongyu Sun: School of Physical Science and Engineering, Beijing Jiaotong University, Beijing 100044, China
Shisong Li: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Energies, 2023, vol. 16, issue 3, 1-27

Abstract: Magnetic flux leakage testing (MFL) is the most widely used nondestructive testing technology in the safety inspection of oil and gas pipelines. The analysis of MFL test data is essential for pipeline safety assessments. In recent years, deep-learning technologies have been applied gradually to the data analysis of pipeline MFL testing, and remarkable results have been achieved. To the best of our knowledge, this review is a pioneering effort on comprehensively summarizing deep learning for MFL detection and evaluation of oil and gas pipelines. The majority of the publications surveyed are from the last five years. In this work, the applications of deep learning for pipeline MFL inspection are reviewed in detail from three aspects: pipeline anomaly recognition, defect quantification, and MFL data augmentation. The traditional analysis method is compared with the deep-learning method. Moreover, several open research challenges and future directions are discussed. To better apply deep learning to MFL testing and data analysis of oil and gas pipelines, it is noted that suitable interpretable deep-learning models and data-augmentation methods are important directions for future research.

Keywords: deep learning; oil and gas pipeline; magnetic flux leakage; object detection; CNN; data augmentation; GAN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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