Intelligent Manufacturing Technology

Magnetic Flux Leakage Course of Inner Defects and Its Detectable Depth

  • Jianbo Wu ,
  • Wenqiang Wu ,
  • Erlong Li ,
  • Yihua Kang
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  • 1. School of Mechanical Engineering, Sichuan University, Chengdu 610065, China;
    2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Received date: 2020-02-27

  Revised date: 2021-02-08

  Online published: 2021-12-21

Supported by

Supported by National Natural Science Foundation of China (Grant Nos. 51907131, 92060114), and Sichuan Science and Technology Program (Grant Nos. 2020YFG0090, 2021YFG0039, 2020ZDZX0024)

Abstract

As a promising non-destructive testing (NDT) method, magnetic flux leakage (MFL) testing has been widely used for steel structure inspection. However, MFL testing still faces a great challenge to detect inner defects. Existing MFL course researches mainly focus on surface-breaking defects while that of inner defects is overlooked. In the paper, MFL course of inner defects is investigated by building magnetic circuit models, performing numerical simulations, and conducting MFL experiments. It is found that the near-surface wall has an enhancing effect on the MFL course due to higher permeability of steel than that of air. Further, a high-sensitivity MFL testing method consisting of Helmholtz coil magnetization and induction coil with a high permeability core is proposed to increase the detectable depth of inner defects. Experimental results show that inner defects with buried depth up to 80.0 mm can be detected, suggesting that the proposed MFL method has the potential to detect deeply-buried defects and has a promising future in the field of NDT.

Cite this article

Jianbo Wu , Wenqiang Wu , Erlong Li , Yihua Kang . Magnetic Flux Leakage Course of Inner Defects and Its Detectable Depth[J]. Chinese Journal of Mechanical Engineering, 2021 , 34(4) : 63 -63 . DOI: 10.1186/s10033-021-00579-y

References

[1] Y Kang, J Wu, Y Sun. The use of magnetic flux leakage testing method and apparatus for steel pipe. Materials Evaluation, 2012, 70(7): 821-827.
[2] F Xu, X Wang, H Wu. Inspection method of cable-stayed bridge using magnetic flux leakage detection: Principle, sensor design, and signal processing. Journal of Mechanical Science and Technology, 2012, 26(3): 661-669.
[3] T A Bubenik, J B Nestlroth, R J Eiber, et al. Magnetic flux leakage (MFL) technology for natural gas pipeline inspection. NDT and E International, 1997, 1(30): 36.
[4] Y Ege, M Coramik. A new measurement system using magnetic flux leakage method in pipeline inspection. Measurement, 2018, 123: 163-174.
[5] A Chotzoglou, M Pissas, A. D Zervaki, et al. Visualization of the rolling contact fatigue cracks in rail tracks with a magneto optical sensor. Journal of Nondestructive Evaluation, 2019, 38(3): 68.
[6] Y Li, J Wilson, G Y Tian. Experiment and simulation study of 3D magnetic field sensing for magnetic flux leakage defect characterisation. NDT & E International, 2007, 40(2): 179-184.
[7] A R Ramírez, J S D Mason, N Pearson. Experimental study to differentiate between top and bottom defects for MFL tank floor inspections. NDT & E International, 2009, 42(1): 16-21.
[8] K Tsukada, M Yoshioka, Y Kawasaki, et al. Detection of back-side pit on a ferrous plate by magnetic flux leakage method with analyzing magnetic field vector. NDT & E International, 2010, 43(4): 323-328.
[9] Y Sun, J Wu, B Feng, et al. An opening electric-MFL detector for the NDT of in-service mine hoist wire. IEEE Sensors Journal, 2014, 14(6): 2042-2047.
[10] H R Weischdel. The inspection of wire ropes in service: a critical review. Materials Evaluation, 1985, 43(13): 1592-1605.
[11] E Altschuler, A Pignotti. Nonlinear model of flaw detection in steel pipes by magnetic flux leakage. NDT & E International, 1995, 28(1): 35-40.
[12] Eickemeyer. Magnetic gage for testing the magnetic conductivity of metals. U.S. patent 413, 338, 1889.
[13] C W Burrows. Method of and apparatus for testing magnetizable objects by magnetic leakage, U.S. patent 1, 322, 405, 1919.
[14] Y Sun, Y Kang. High-speed magnetic flux leakage technique and apparatus based on orthogonal magnetization for steel pipe. Materials Evaluation, 2010, 68(4): 452-458.
[15] Yanhua S, Yihua K. The feasibility of Omni-directional defects MFL inspection under a unidirectional magnetization. International Journal of Applied Electromagnetics and Mechanics, 2010, 33: 919-925
[16] J Wu, H Fang, X Huang, et al. An online MFL sensing method for steel pipe based on the magnetic guiding effect. Sensors, 2017, 17(12): 2911.
[17] Y Ma, R He, J Chen. A method for improving SNR of drill pipe leakage flux testing signals by means of magnetic concentrating effect. IEEE Transactions on Magnetics, 2015, 51(9): 1-7.
[18] A Joshi, L Udpa, S Udpa, et al. Adaptive wavelets for characterizing magnetic flux leakage signals from pipeline inspection. IEEE Transactions on Magnetics, 2006, 42(10): 3168-3170.
[19] S M Dutta, F H Ghorbel, R K Stanley. Dipole modeling of magnetic flux leakage. IEEE Transactions on Magnetics, 2009, 45(4): 1959-1965.
[20] J Wu, Y Sun, Y Kang, et al. Theoretical analyses of MFL signal affected by discontinuity orientation and sensor-scanning direction. IEEE Transactions on Magnetics, 2014, 51(1): 1-7.
[21] S Yang, Y Sun, L Udpa, et al. 3D simulation of velocity induced fields for nondestructive evaluation application. IEEE Transactions on Magnetics, 1999, 35(3): 1754-1756.
[22] J Wu, Y Sun, B Feng, et al. The effect of motion-induced eddy current on circumferential magnetization in MFL testing for a steel pipe. IEEE Transactions on Magnetics, 2017, 53(7): 1-6.
[23] Y K Shin, W Lord. Numerical modeling of moving probe effects for electromagnetic nondestructive evaluation. IEEE Transactions on Magnetics, 1993, 29(2): 1865-1868.
[24] B T Bastian, N Jaspreeth, S K Ranjith, et al. Visual inspection and characterization of external corrosion in pipelines using deep neural network. NDT & E International, 2019, 107: 102134.
[25] A A Carvalho, J M A Rebello, L V S Sagrilo, et al. MFL signals and artificial neural networks applied to detection and classification of pipe weld defects. NdT & E International, 2006, 39(8): 661-667.
[26] A Khodayari-Rostamabad, J P Reilly, N K Nikolova, et al. Machine learning techniques for the analysis of magnetic flux leakage images in pipeline inspection. IEEE Transactions on Magnetics, 2009, 45(8): 3073-3084.
[27] ISO. ISO 9402 First edition. Seamless and welded (except submerged arc-welded) steel tubes for pressure purposes-Full peripheral magnetic transducer/flux leakage testing of ferromagnetic steel tubes for the detection of longitudinal imperfection. 1989-07-01.
[28] J Wu, F Hui, L Long, et al. The signal characteristics of rectangular induction coil affected by sensor arrangement and scanning direction in MFL application. International Journal of Applied Electromagnetics and Mechanics, 2016, 52(3-4): 1257-1265.
[29] A Sophian, G Y Tian, S Zairi. Pulsed magnetic flux leakage techniques for crack detection and characterisation. Sensors and Actuators A: Physical, 2006, 125(2): 186-191.
[30] X Lu, G Li, L Chen, et al. Study on low frequency AC excitation magnetization magnetic flux leakage testing for defects with different depths. ASME Pressure Vessels and Piping Conference (PVP 2018), Prague, Czech Republic: JUL 15-20, 2018.
[31] K Tsukada, Y Majima, Y Nakamura, et al. Detection of inner cracks in thick steel plates using unsaturated AC magnetic flux leakage testing with a magnetic resistance gradiometer. IEEE Transactions on Magnetics, 2017, 53(11): 1-5.
[32] J A Parra-Raad, S Roa-Prada. Multi-objective optimization of a magnetic circuit for magnetic flux leakage-type non-destructive testing. Journal of Nondestructive Evaluation, 2016, 35(1): 14.
[33] Jianbo W, Hui F, Jie W, et al. The influence of non-uniform wall thickness on MFL testing for a steel pipe. Insight-Non-Destructive Testing and Condition Monitoring, 2015, 57(12): 703-708.
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