Defect Size Quantification for Pipeline Dynamic MFL Detection via 3-D Dynamic Magnetic Dipole Forward Model

Magnetic flux leakage (MFL) detection has been attracted much attention in evaluating the pipeline integrity due to its high efficiency. The precise calculation of the MFL signals plays an important role in the defect size quantification. Currently, finite element model (FEM) and magnetic dipole mod...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 74; S. 1 - 10
Hauptverfasser: Zhong, Yuan, Wang, Gang, Yu, Xue, Jia, Liang, Wang, Liwei, Zhang, Huaguang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Autoencoders
Defect size quantification
Defects
Finite element analysis
Finite element method
magnetic dipole model (MDM)
Magnetic dipoles
Magnetic domains
Magnetic flux
Magnetic flux leakage
magnetic flux leakage (MFL)
Magnetization
Mathematical models
Noise reduction
Nonlinear distortion
Pipelines
Saturation magnetization
signal reconstruction
stacked denoising autoencoder (SDAE)
Three-dimensional displays
ISSN:0018-9456, 1557-9662
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Zusammenfassung:Magnetic flux leakage (MFL) detection has been attracted much attention in evaluating the pipeline integrity due to its high efficiency. The precise calculation of the MFL signals plays an important role in the defect size quantification. Currently, finite element model (FEM) and magnetic dipole model (MDM) are two main methods, but FEMs are time-consuming and MDM cannot generate the dynamic MFL signals. To solve the above problems, a defect size quantification method for pipeline dynamic MFL detection via 3-D dynamic magnetic dipole forward model (3-D DMDFM) is proposed. First, a 3-D DMDFM is established, where the dynamic influencing factors, such as the speed, lift-off, and magnetization angle, are integrated into 3-D MDM, so that 3-D DMDFM can generate dynamic MFL signals. Second, to solve the problem of MFL signals distortion due to the dynamic influencing factors, a layer-skipping stacked denoising autoencoder (LS-SDAE) is proposed to reconstruct the MFL signals accurately, where two layer-skipping denoising autoencoders (DAEs) are used to supervise SDAE and learn the underutilized features from the nondirect connection feature layer. Simulated and experimental results show that 3-D DMDFM has both high efficiency and low error when ignoring the modeling complexity. Finally, the reconstructed MFL signals are used to quantify the defect size, and the minimum relative error of the defect depth quantification can reach 1.33%.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2025.3541813