Review on automated condition assessment of pipelines with machine learning

Y Liu, Y Bao - Advanced Engineering Informatics, 2022 - Elsevier
Pipelines carrying energy products play vital roles in economic wealth and public safety, but
incidents continue occurring. Condition assessment of pipelines is essential to identify …

Analysis of magnetic-flux leakage (MFL) data for pipeline corrosion assessment

X Peng, U Anyaoha, Z Liu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Oil and gas pipelines transport and distribute large quantities of oil products and natural gas
to industrial and residential customers over a long distance. However, pipeline failures could …

Magnetic particle inspection: Status, advances, and challenges—Demands for automatic non-destructive testing

Q Wu, K Dong, X Qin, Z Hu, X **ong - Ndt & E International, 2024 - Elsevier
Magnetic particle inspection (MPI) is a highly sensitive and user-friendly nondestructive
technique that remains essential for detecting surface and near-surface defects in …

An estimation method of defect size from MFL image using visual transformation convolutional neural network

S Lu, J Feng, H Zhang, J Liu… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
In most current nondestructive testing systems, a magnetic flux leakage (MFL) method is
widely used in various industry fields, where the structural integrity of specimens is of vital …

Injurious or noninjurious defect identification from MFL images in pipeline inspection using convolutional neural network

J Feng, F Li, S Lu, J Liu, D Ma - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
This paper proposes an injurious or noninjurious defect identification method from magnetic
flux leakage (MFL) images based on convolutional neural network. Different from previous …

Deep learning for magnetic flux leakage detection and evaluation of oil & gas pipelines: A review

S Huang, L Peng, H Sun, S Li - Energies, 2023 - mdpi.com
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 …

Estimation of defect size and cross-sectional profile for the oil and gas pipeline using visual deep transfer learning neural network

M Zhang, Y Guo, Q **e, Y Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The magnetic flux leakage (MFL) defect detection of oil and gas pipelines faces two tasks,
defect type identification and defect size and shape estimation. However, there are few …

Detection and sizing of metal-loss defects in oil and gas pipelines using pattern-adapted wavelets and machine learning

M Layouni, MS Hamdi, S Tahar - Applied Soft Computing, 2017 - Elsevier
Signals collected from the magnetic scans of metal-loss defects have distinct patterns.
Experienced pipeline engineers are able to recognize those patterns in magnetic flux …

Quantitative study on the propagation characteristics of MFL signals of outer surface defects in long-distance oil and gas pipelines

B Liu, Y Liang, L He, Z Lian, H Geng, L Yang - NDT & E International, 2023 - Elsevier
The magnetic flux leakage (MFL) internal detection is one of the most effective methods for
assessment of long-distance oil and gas pipelines. To quantify the outer surface defect …

A novel crack quantification method for ultra-high-definition magnetic flux leakage detection in pipeline inspection

Y Long, J Zhang, S Huang, L Peng… - IEEE Sensors …, 2022 - ieeexplore.ieee.org
Cracks that may cause pipeline cracking and leakage become the main risk of in-service
pipelines after conventional metal loss defects have been detected. Therefore, it is …