Affiliation:
1. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
2. School of Physical Science and Engineering, Beijing Jiaotong University, Beijing 100044, 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.
Funder
National Natural Science Foundation of China
State Key Laboratory of Power System and Generation Equipment
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference107 articles.
1. Torsional waves excited by electromagnetic-acoustic transducers during guided-wave acoustic inspection of pipelines;Murashov;Acoust. Phys.,2016
2. Long-term stability of guided wave electromagnetic acoustic transducer systems;Herdovics;Struct. Health Monit.,2020
3. Sizing of multiple cracks using magnetic flux leakage measurements;Ravan;IET Sci. Meas. Technol.,2016
4. A Simplified lift-off correction for three components of the magnetic flux leakage signal for defect detection;Peng;IEEE Trans. Instrum. Meas.,2021
5. Wasif, R., Tokhi, M.O., Shirkoohi, G., Marks, R., and Rudlin, J. (2022). Development of permanently installed magnetic eddy current sensor for corrosion monitoring of ferromagnetic pipelines. Appl. Sci., 12.
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