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 Xiong - 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 …

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 …

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 …

A simplified lift-off correction for three components of the magnetic flux leakage signal for defect detection

L Peng, S Huang, S Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Magnetic flux leakage (MFL) testing is widely used to detect and estimate defect in
ferromagnetic materials. The lift-off of sensors caused the weakening of the detected signal …

Nanofluid based optical sensor for rapid visual inspection of defects in ferromagnetic materials

V Mahendran, J Philip - Applied Physics Letters, 2012 - pubs.aip.org
We have developed a simple sensor for imaging internal defects in materials using a
magnetically polarizable nanoemulsion. The gradient in the magnetic flux lines around the …

Estimation of depth and length of defects from magnetic flux leakage measurements: verification with simulations, experiments, and pigging data

MR Kandroodi, BN Araabi, MM Bassiri… - IEEE Transactions …, 2016 - ieeexplore.ieee.org
Magnetic flux leakage (MFL) method is the most widely used non-destructive testing
techniques for detection and characterization of defects in product transmission pipelines …