Journal of Nondestructive Evaluation

, Volume 32, Issue 1, pp 37–43 | Cite as

Characterization of Wire Rope Defects with Gray Level Co-occurrence Matrix of Magnetic Flux Leakage Images

  • Donglai Zhang
  • Min Zhao
  • Zhihui Zhou
  • Shimin Pan


Magnetic flux leakage (MFL) techniques are used extensively for non-intrusively detecting and characterizing wire rope defects. Traditionally, MFL signals are captured with induction coil sensors. However, the output of coil sensors is related to the wire rope speed, and they can only provide the axial distribution along the wire rope. Hall sensors array are designed due to the limitation of coil sensors. In this paper, a Hall sensors array was designed to capture the MFL signals both axially and circumferentially. 30-channel data from Hall sensors are processed to compose a MFL image. A digital image process technique is introduced to preprocess the MFL image, the MFL images from different types of defects show different texture characteristics. Gray level co-occurrence matrix is utilized for feature extraction of the texture in the MFL image. Five typical eigenvalues (contrast, correlation, energy, homogeneity and entropy) are used as the inputs of back propagation (BP) networks. After training with typical samples, the BP networks show good performance in the quantitative recognition of different defects. The result of this work shows that texture analysis method for MFL image is suitable for feature extraction and quantitative detection of wire rope defects.


Magnetic flux leakage image Wire rope defects Feature extraction Gray level co-occurrence matrix 


  1. 1.
    Weischedel, H.R.: The inspection of wire ropes in service: a critical review. Mater. Eval. 46(5), 430–437 (1988) Google Scholar
  2. 2.
    Stanley, R.K.: Simple explanation of the theory of the total magnetic flux method for the measurement of ferromagnetic cross sections. Mater. Eval. 53(1), 72–75 (1995) Google Scholar
  3. 3.
    Dutta, S.M., Ghorebel, F.H., Stanley, R.K.: Simulation and analysis of 3-D magnetic flux leakage. IEEE Trans. Magn. 45(4), 1966–1972 (2009) CrossRefGoogle Scholar
  4. 4.
    Mukhopadhyay, S., Srivastava, G.P.: Characterisation of metal loss defects from magnetic flux leakage signals with discrete wavelet transform. NDT E Int. 33(1), 57–65 (2000) CrossRefGoogle Scholar
  5. 5.
    Cao, Y., Zhang, D., Wang, C., et al.: More accurate localized wire rope testing based on Hall sensor array. Mater. Eval. 64(9), 907–910 (2006) Google Scholar
  6. 6.
    Sharatchandra Singh, W., Rao, B.P.C., Vaidyanathan, S., et al.: Detection of leakage magnetic flux from near-side and far-side defects in carbon steel plates using a giant magneto-resistive sensor. Meas. Sci. Technol. 19, 1–8 (2008) Google Scholar
  7. 7.
    Krause, H.J., Kreutzbruck, M.V.: Recent developments in SQUID NDE. Physica C, Supercond. 368, 70–79 (2002) CrossRefGoogle Scholar
  8. 8.
    Gu, W., Chu, J.: A transducer made up of fluxgate sensors for testing wire rope defects. IEEE Trans. Instrum. Meas. 51(1), 120–124 (2002) CrossRefGoogle Scholar
  9. 9.
    Jomdecha, C., Prateepasen, A.: Design of modified electromagnetic main-flux for steel wire rope inspection. NDT E Int. 42, 77–83 (2009) CrossRefGoogle Scholar
  10. 10.
    Sharatchandra, W., Rao, B.P.C., Thirunavukkarasu, S., et al.: Flexible GMR sensor array for magnetic flux leakage testing of steel track ropes. J. Sens., 1–6 (2012) Google Scholar
  11. 11.
    Bharath Kumar, S.V., Ramaswamy, S.: A texture analysis approach for automatic flaw detection in pipelines. In: International Conference on Signal Processing & Communications, pp. 320–323 (2004) Google Scholar
  12. 12.
    Platzer, E.-S., Süße, H., Nägele, J., et al.: On the suitability of different features for anomaly detection in wire ropes. Comput. Sci. 68(4), 296–308 (2010) Google Scholar
  13. 13.
    Carvalhoa, A.A., Rebello, J.M.A., Sagrilo, L.V.S., et al.: MFL signals and artificial neural networks applied to detection and classification of pipe weld defects. NDT E Int. 39, 661–667 (2006) CrossRefGoogle Scholar
  14. 14.
    Ramuhalli, P., Udpa, L., Udpa, S.S.: Electromagnetic NDE signal inversion by function-approximation neural networks. IEEE Trans. Magn. 38(6), 3633–3642 (2002) CrossRefGoogle Scholar
  15. 15.
    Zhang, D., Zhao, M., Zhou, Z.: Quantitative inspection of wire rope discontinuities using magnetic flux leakage imaging. Mater. Eval. 70(7), 872–878 (2012) Google Scholar
  16. 16.
    Haller, A., Dübendorf, E.: Wire cable testing using high resolution magnetic induction. E-J Nondestr. Test. 3(2) (1998). [Online] Available:
  17. 17.
    Mukherjee, D., Saha, S., Mukhopadhyay, S.: An adaptive channel equalization algorithm for MFL signal. NDT E Int. 45, 111–119 (2012) CrossRefGoogle Scholar
  18. 18.
    Zhang, Y., Ye, Z., Xu, X.: An adaptive method for channel equalization in MFL inspection. NDT E Int. 40, 127–139 (2007) CrossRefGoogle Scholar
  19. 19.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610–621 (1973) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Donglai Zhang
    • 1
  • Min Zhao
    • 1
  • Zhihui Zhou
    • 1
  • Shimin Pan
    • 1
  1. 1.Harbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina

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