Study on the Influence of Spacing of the Nearby Corrosion Defects on Magnetic Flux Leakage Signals

Document Type : Research Article

Authors

1 Department of mechanical engineering,Iran university of science and technology, Tehran, Iran

2 Department of mechanical engineering, Iran university of science and technology, Tehran, Iran

Abstract

Magnetic flux leakage technique is a widely used and effective approach for detecting and sizing of the corrosion defects in ferromagnetic pipelines. In general, corrosion defects occur in dense clusters and affect each other. However due to the interaction between the magnetic flux leakage signals, these defects can-not be accurately characterized using the traditional magnetic flux leakage method. In order to discriminate the individual defects and improve sizing performance, tri-axial magnetic flux leakage technique is used. The study is performed using the extensive finite element modeling focusing on the spatial distribution of tri-axial magnetic flux leakage components produced by the nearby corrosion defects. This type of defect geometry comprises two pits that are sufficiently close to influence flux distributions in the area between them. Various degrees of closeness are considered by varying the spacing of the two pits. Following the simulations, experimental magnetic flux leakage tests are performed on the steel plates containing nearby pits. The experimental and finite element modeling results indicate that combining the axial, radial and tangential magnetic flux leakage data can discriminate and characterize the nearby pits. Finally, the experimental and finite element modeling results are compared and validated.

Keywords

Main Subjects


 [1] Y. Bai, Pipelines and risers, Elsevier, 2001
[2] M. Afzal, S. Udpa, Advanced signal processing of magnetic flux leakage data obtained from seamless gas pipeline, Ndt & E International, 35(7) (2002) 449-457
[3] J.W. Smith, B.R. Hay, Magnetic flux leakage inspection tool for pipelines, in, Google Patents, 2000
[4] M.B. Ignagni, Apparatus and method for accurate pipeline surveying, in, Google Patents, 2003.
[5] Y. Gunaltun, D. Supriyatman, J. Achmad, Top-ofline corrosion in gas lines confirmed by condensation analysis, Oil & gas journal, 97(28) (1999) 64-64
[6] C. Argent, Macaw’s pipeline defects. sl: Yellow PencilMarketing, 2003, ISBN 0-9544295-0-8
[7]  D.V. Pugh, S.L. Asher, J. Cai, W.J. Sisak, J.L. Pacheco,F.C. Ibrahim, E.J. Wright, A. Dhokte, S. Venaik, D. Robson, Top-of-line corrosion mechanism for sour wet gas pipelines, in: CORROSION 2009, NACE International, 2009
[8]  M. Singer, D. Hinkson, Z. Zhang, H. Wang, S. Nešić,CO2 top-of-the-line corrosion in presence of acetic acid: a parametric study, Corrosion, 69(7) (2013) 719-735
[9]  A. Benjamin, J. Freire, R. Vieira, Part 6: Analysisof pipeline containing interacting corrosion defects, Experimental Techniques, 31(3) (2007) 74-82.
[10] Y. Gunaltun, R. Piccardino, D. Vinazza, Interpretation of MFL and UT inspection results in case of top of line corrosion, CORROSION/2006, paper, (6170) (2006) .
[11] N.A. Jemari, J. Palmer, T. Beuker, J. Baker, B.Wimolsukpirakul, J. Onderdonk, A. van Roodselaar, L. Huyse, Improvements in the accurate estimation of top of the line internal corrosion of subsea pipelines on the basis of in-line inspection data, in: 2010 8th International Pipeline Conference, American Society of Mechanical Engineers, 2010, pp. 75-82.
[12] M.A. Siebert, J.E. Sutherland, Application of the circumferential component of magnetic flux leakage measurement for in-line inspection of pipelines, BJ Pipeline Inspection Services, Calgary, Alberta (CA), 1999
[13]  Y. Li, J. Wilson, G. Y.  Tian, Experiment and simulation study of 3D magnetic field sensing for magnetic flux leakage defect characterization, NDT and E International, 40(2) (2007) 179-184.
[14]  S.M. Dutta, F.H. Ghorbel, R.K. Stanley, Dipole modeling of magnetic flux leakage, IEEE Transactions on Magnetics, 45(4) (2009) 1959-1965.
[15]  G. Kopp, H. Willems, Sizing limits of metal loss anomalies using tri-axial MFL measurements: A model study, NDT & E International, 55 (2013) 75-81.
[16]  J. Chen, S. Huang W. Zhao, Three-dimensional defect inversion from magnetic flux leakage signals using iterative neural network, IET Science Measurement & Technology, 9(4) (2015) 418–426.
[17]  M. Layouni, M.S. Hamdi, S. Tahar, Detection and sizing of metal-loss defects in oil and gas pipelines using pattern-adapted wavelets and machine learning, Applied Soft Computing, 52 (2017) 247-261.
[18]  J.S. Alaric, V. Suresh, A. Abudhahir, M.C. Sobia, M. Baarkavi, Theoretical Analysis of the Rectangular Defect Orientation using Magnetic Flux Leakage, Measurement Science Review, 18(1) (2018) 28-34.
[19] Hwang, W. Lord, Finite element modeling of magnetic field/ defect interactions, Journal of Testing and Evaluation, 3(1) (1975) 21 -25.
[20]  V. Shcherbinin, N. Zatsepin, Calculation of the magneto static field of surface defects. 1. Field topography of defect models, Defectoscopy, 5 (1966) 385-393.
[21]  J. Jackson, Classical Electrodynamics (Wiley, New York) (1998), Google Scholar, 78.
[22]   Z. Wang, Y. Gu, Y. Wang, A review of three magnetic NDT technologies, Journal of Magnetism and Magnetic Materials, 324(4) (2012) 382-388.
[23] H. Jansen, P. van de Camp, M. Geerdink, Magnetization as a Key Parameter of MagneticFlux Leakage Pigs for Pipeline Inspection, NDT & E International, 1(30) (1997) 35.
[24]   G.S. Park, E.S. Park, Improvement of the sensor system in magnetic flux leakage-type nondestructive testing (NDT), IEEE Transactions on Magnetics, 38(2) (2002) 1277-1280.
[25]  T. Bubenik, J. Nestlroth, R. Eiber, B. Saffell,Magnetic flux leakage (MFL) technology for natural gas pipeline inspection, NDT and E International, 1(30) (1997) 36.
[26]  L. Clapham, D.L. Atherton, Stress effects on MFLsignals, in: CORROSION 2002, NACE International, 2002
[27]  A. Belanger, Managing HIC-Affected PipelinesUsing Multiple-Technology Hard-Spot Tools, in: Conference Proceedings Pipeline Pigging, Integrity assessment & Repair, 2004.
[28]  K. Reber, Reliability of flaw size calculation based on magnetic flux leakage inspection of pipelines, (2006).
[29] J. Qi, S. Qingmei, L. Nan, Z. Paschalis, W. Jihong,Detection and estimation of oil-gas pipeline corrosion defects, in: Proceedings of the 18th international conference on systems engineering (ICSE 2006), 2006, pp. 173-177 .
[30]  A. Joshi, L. Udpa, S. Udpa, A. Tamburrino, Adaptivewavelets for characterizing magnetic flux leakage signals from pipeline inspection, IEEE transactions on magnetics, 42(10) (2006) 3168-3170.
[31] M.R. Kandroodi, F. Shirani, B.N. Araabi, M.N.Ahmadabadi, M.M. Bassiri, Defect detection and width estimation in natural gas pipelines using MFL signals, in: Control Conference (ASCC), 2013 9th Asian, IEEE, 2013, pp. 1-6.
[32] J.-W. Kim, S. Park, Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation, Sensors, 18(1) (2018) 109.