Theoretical Analysis on the Distribution of Eddy Current in Motion-Induced Eddy Current Testing and High-Speed MFL Testing

Feng, Bo; Deng, Kangxuan; Wang, Shenghan; Chen, Shukai; Kang, Yihua

doi:10.1007/s10921-022-00891-0

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…Recently, B. Feng found an analytical solution to Equation (9) and further obtained the expression of the motion-induced eddy current under a pole of the magnetizer [ 51 ]:

where J total is the eddy current in specimen, y 0 is the liftoff, h is the height of magnetizer,

and

are coefficients that are solved as [ 51 ]:

…”

Section: Factors Influencing Mfl Signalmentioning

confidence: 99%

“…With the analytical solution of the MIEC, the tail effect and tilt angle of the MIEC at different moving speeds are also analyzed [ 51 ].…”

Section: Factors Influencing Mfl Signalmentioning

confidence: 99%

“…Antipov used an induced tail magnetic field to test rails at high-speed [ 66 ]. B. Feng, T. Rocha, and F. Yuan all studied the motion-induced eddy current testing method and used magnetic field sensors to pick up the defect signals [ 51 , 67 , 68 , 69 , 70 , 71 , 72 ]. Researchers from Technische Universität Ilmenau proposed a new Lorentz force NDT method for conductive specimens, which is also based on motion-induced eddy current [ 73 , 74 , 75 , 76 , 77 ].…”

Section: Factors Influencing Mfl Signalmentioning

confidence: 99%

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A Review of Magnetic Flux Leakage Nondestructive Testing

Feng

et al. 2022

Materials

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Magnetic flux leakage (MFL) testing is a widely used nondestructive testing (NDT) method for the inspection of ferromagnetic materials. This review paper presents the basic principles of MFL testing and summarizes the recent advances in MFL. An analytical expression for the leakage magnetic field based on the 3D magnetic dipole model is provided. Based on the model, the effects of defect size, defect orientation, and liftoff distance have been analyzed. Other influencing factors, such as magnetization strength, testing speed, surface roughness, and stress, have also been introduced. As the most important steps of MFL, the excitation method (a permanent magnet, DC, AC, pulsed) and sensing methods (Hall element, GMR, TMR, etc.), have been introduced in detail. Finally, the algorithms for the quantification of defects and the applications of MFL have been introduced.

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“…Recently, B. Feng found an analytical solution to Equation (9) and further obtained the expression of the motion-induced eddy current under a pole of the magnetizer [ 51 ]:

where J total is the eddy current in specimen, y 0 is the liftoff, h is the height of magnetizer,

and

are coefficients that are solved as [ 51 ]:

…”

Section: Factors Influencing Mfl Signalmentioning

confidence: 99%

“…With the analytical solution of the MIEC, the tail effect and tilt angle of the MIEC at different moving speeds are also analyzed [ 51 ].…”

Section: Factors Influencing Mfl Signalmentioning

confidence: 99%

Section: Factors Influencing Mfl Signalmentioning

confidence: 99%

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A Review of Magnetic Flux Leakage Nondestructive Testing

Feng

et al. 2022

Materials

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“…Some researchers have used high frequency eddy current measurements for the detection of stress defects [8]. The high frequency excitation of eddy current testing modifies the secondary electromagnetic field inside the ferromagnetic material.…”

Section: Introductionmentioning

confidence: 99%

A stress defect state measurement method based on low-frequency ACMFL excitation and Hall sensor array collection

Zhang

Jian

et al. 2023

Meas. Sci. Technol.

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The safety testing of ferromagnetic materials, which are the main materials for various machines and equipment, is particularly important. Stress concentration zone (stress defects) cause stress corrosion of ferromagnetic materials, and it also has the possible to cause direct damage to ferromagnetic materials. Estimation of stress sources state using electromagnetic nondestructive measurement method s is a critical and difficult problem. In this paper, a visual and intelligent identification method of stress defects in ferromagnetic materials by low frequency AC magnetic flux leakage (ACMFL) technique is proposed. A new three-point compression experiment was designed in this paper. Time-difference vision is established to analyze the ACMFL signal caused by stress defects. A visual transformed convolutional neural network (vt-CNN) deep learning algorithm has been proposed to identify grayscale patterns pre-processed by the time-difference vision. The results show that the method proposed in this paper elucidates the relationship between the time-difference vision of a stress defect and the stress source state of the mechanical stress. Our proposed method allows to analyze the pressure indenter size of the pressure source of stress defects.

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