Magnetic flux leakage detection method by the arrays consisted of three-dimensional hall sensor for rail top surface cracks

Gong, Wendong; Yang, Tao; Guo, Lanhui

doi:10.1080/00150193.2022.2091999

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…With the development of MFL testing technology, a growing concern is the accuracy and effectiveness of defect size estimation, and hence the quantitative method has become the mainstream of research. The relationship between the leakage magnetic field distribution and the defect characteristics has been studied extensively using laboratory experiments [12,13], analytic analysis with the magnetic dipole model (MDM) [14,15] and the finite-element model [16,17]. This enables one to extract defect features based on statistical methods and signal processing techniques, which results in a well-established defect feature library [18].…”

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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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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“…To accurately and quantitatively detect the degree of wire rope damage, Ref. [ 21 ] selects a higher precision and a greater number of sensors. In terms of algorithms, Refs.…”

Section: Introductionmentioning

confidence: 99%

A Novel Defect Detection Method for Overhead Ground Wire

Xiao,

Xiong,

Zhang

et al. 2023

Sensors

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Overhead ground wires typically have strong axial tension and are prone to structural defects caused by corrosion and lightning strikes, which could lead to serious safety hazards. Therefore, it is important to detect defects accurately and quickly to avoid those problems. Existing defect detection methods for overhead ground wires are mainly traditional metal defect detection methods, including eddy current detection, ultrasonic detection, and manual visual inspection. However, those methods have problems of low detection efficiency, high environmental requirements, and insufficient reliability. To solve the above problems, this paper studies a novel type of defect detection technology for overhead ground wire. Firstly, the magnetic leakage characteristics around the defects of overhead ground wires are analyzed, and the defect detection device is designed. Then, the influence of air gap, lift-off distance, defect width, and cross-sectional loss rate on the magnetic flux leakage signal is studied, a novel defect detection method for overhead ground wire is proposed, and experimental verification is carried out. The results show that the proposed method can accurately locate and quantify the defect, which has the advantages of good reliability and high efficiency and lays the foundation for preventing accidents caused by defective overhead ground wires.

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Design and Development of Automatic Detection System for Locomotive Wheel Rims RCF Cracks Based on ACFM

Wang,

Yuan,

et al. 2023

2023 IEEE 11th International Conference on Information, Communication and Networks (ICICN)

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Magnetic flux leakage detection method by the arrays consisted of three-dimensional hall sensor for rail top surface cracks

Cited by 5 publications

References 15 publications

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

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

A Novel Defect Detection Method for Overhead Ground Wire

Design and Development of Automatic Detection System for Locomotive Wheel Rims RCF Cracks Based on ACFM

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