A novel method for defects marking and classifying in MFL inspection of pipeline

Pan, Jianhua; Gao, Lun

doi:10.1016/j.ijpvp.2023.104892

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…They then utilized the SSA_BP neural network for extracting and classifying three-dimensional MFL feature signals from these marked regions. The findings from their study reveal that this approach enhances the efficiency of defect marking and provides a more detailed analysis of the marked areas [37].…”

Section: (B) Eddy Current Testingmentioning

confidence: 99%

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

Huang,

Chen,

et al. 2024

Sensors

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The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

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Section: (B) Eddy Current Testingmentioning

confidence: 99%

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

Huang,

Chen,

et al. 2024

Sensors

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“…Hence, those defect features are restricted in the field applications to cover all characteristics of MFL testing regarding pipeline defects. Then, artificial intelligence methods are adopted for MFL testing in support of defect quantification [19–23]. The study's findings demonstrate that the machine learning method (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The study's findings demonstrate that the machine learning method (e.g. support vector machine, SVM, neural network) can be used effectively to establish a functional mapping between MFL data and defect size [19][20][21]. However, those methods are mainly developed based on conventional machine learning models with shallow configurations and rely heavily on the experts' experiences to manually design features.…”

Section: Introductionmentioning

confidence: 99%

Magnetic flux leakage defect size estimation method based on physics-informed neural network

Xiong,

Liu,

Hou

et al. 2023

Phil. Trans. R. Soc. A.

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Magnetic flux leakage (MFL) is a magnetic method of non-destructive testing for in-pipe defect detection and sizing. Despite the fact that recent developments in machine learning have revolutionized disciplines like MFL defect size estimation, the most current methods for quantifying pipeline defects are primarily data-driven, which may violate the underlying physical knowledge. This paper proposes a physics-informed neural network-based method for MFL defect size estimation. The training process of neural network is guided by the MFL data and the physical constraints that is mathematically represented by the magnetic dipole model. We use synthetic MFL data produced by a virtual MFL testing of pipeline defects to validate the proposed method through a comparison to purely data-driven neural networks and support vector machines. The findings imply that the physics-informed strategy can both improve predictive accuracy and mitigate physical violations in MFL testing, providing us with a better knowledge of how neural networks perform in defect size estimation. This article is part of the theme issue ‘Physics-informed machine learning and its structural integrity applications (Part 2)’.

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A classification and quantitative assessment method for internal and external surface defects in pipelines based on ASTC-Net

Yuan,

Qiao,

et al. 2024

Advanced Engineering Informatics

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A novel method for defects marking and classifying in MFL inspection of pipeline

Cited by 11 publications

References 11 publications

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

Magnetic flux leakage defect size estimation method based on physics-informed neural network

A classification and quantitative assessment method for internal and external surface defects in pipelines based on ASTC-Net

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