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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Songling Huang & Lisha Peng & Hongyu Sun & Shisong Li, 2023. "Deep Learning for Magnetic Flux Leakage Detection and Evaluation of Oil & Gas Pipelines: A Review," Energies, MDPI, vol. 16(3), pages 1-27, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1372-:d:1050042
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    References listed on IDEAS

    as
    1. Jinyao Duan & Kai Song & Wenyu Xie & Guangming Jia & Chuang Shen, 2022. "Application of Alternating Current Stress Measurement Method in the Stress Detection of Long-Distance Oil Pipelines," Energies, MDPI, vol. 15(14), pages 1-20, July.
    2. Saksham Jain & Gautam Seth & Arpit Paruthi & Umang Soni & Girish Kumar, 2022. "Synthetic data augmentation for surface defect detection and classification using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1007-1020, April.
    3. Lisha Peng & Shisong Li & Hongyu Sun & Songling Huang, 2022. "A Pipe Ultrasonic Guided Wave Signal Generation Network Suitable for Data Enhancement in Deep Learning: US-WGAN," Energies, MDPI, vol. 15(18), pages 1-12, September.
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