Journal of Failure Analysis and Prevention

, Volume 19, Issue 2, pp 551–560 | Cite as

Nondestructive Testing of Wire Ropes Based on Image Fusion of Leakage Flux and Visible Light

  • Juwei Zhang
  • Shilei WangEmail author
Technical Article---Peer-Reviewed


Magnetic flux leakage (MFL) detection has the advantages of obvious target defect and accurate positioning. The MFL information on the surface of the wire rope can be converted into magnetic images so that the axial and circumferential position of the wire rope defects can be displayed more intuitively. The visible image has rich texture information. By fusing the visible and magnetic images, we can make full use of their information and the defect recognition rate can be improved. In this paper, the original magnetic leakage data are first denoised by wavelet soft threshold, and the corresponding visible image is processed by homomorphic filtering to eliminate the interference of light. Then, the magnetic image and visible image are fused at feature level. The features of magnetic image and visible image are extracted and fused, and then principal component analysis is carried out to reduce dimensions. The fusion feature vector is input into back-propagation network for recognition and is compared with the magnetic image features alone. Experimental results show that when the error is allowed to be 0.9%, the defect recognition rate after image fusion is 5.27% higher than the magnetic image.


Wire rope Image fusion Visible Magnetic 



This work is partially supported by the National Natural Science Foundation of China (Grant No. 61040010, 61172014, U1504617), the Key Technologies R&D Program of Henan Province (Grant No. 152102210284), the Science and Technology Program of Henan Education Department (Grant No. 17A510009), the Science and Technology Open Cooperation Program of Henan province (Grant No. 182106000026).

Author Contributions

Juwei Zhang conceived, directed and designed the all work; Shilei Wang performed the experiments, analyzed the data and wrote the paper.

Compliance with Ethical Standards

Conflicts of interest

The authors declare no conflict of interest.


  1. 1.
    Y.N. Cao, D.L. Zhang, D.G. Xu, Study on algorithms of wire rope localized flaw quantitative analysis based on three-dimensional magnetic flux leakage. Acta Electron. Sin. 6(35), 1170–1173 (2017)Google Scholar
  2. 2.
    Y.N. Cao, in Study on wire rope local flaw quantitative testing based on MFL imaging principle. Ph.D. thesis, Harbin Institute of Technology (2007).Google Scholar
  3. 3.
    M. Zhao, D.L. Zhang, Z. Zhou, in Channel equalization method for MFL signals of wire rope defects. J. Harbin Inst. Technol. 9(45), 47–51.Google Scholar
  4. 4.
    D.H. Wu, L.X. Su, X.H. Wang, A novel non-destructive testing method by measuring the change rate of magnetic flux leakage. J. Nondestruct. Eval 2(36), 24 (2017)Google Scholar
  5. 5.
    J.W. Zhang, X.J. Tan, Quantitative inspection of remanence of broken wire rope based on compressed sensing. Sensors 9(16), 1366 (2016)CrossRefGoogle Scholar
  6. 6.
    J.W. Zhang, P.B. Zheng, X.J. Tan, Recognition of broken wire rope based on remanence using EEMD and wavelet. Methods, Sensors 4(18), 1110 (2018)CrossRefGoogle Scholar
  7. 7.
    W.L. Li, W.J. Feng, Z.Z. Li, C.Z. Yan, Dimension design of excitation structure for wire rope nondestructive testing. J. Tongji Univ. 12(40), 1888–1893 (2012)Google Scholar
  8. 8.
    H.J. Chen, J. Zheng, Y.H. Kang, X.J. Wu, A Sensitivity prediction method based on limit flaw set for evaluating wire rope EMT instrument testing capability on broken wire. J. Basic Sci. Eng. (2009).Google Scholar
  9. 9.
    D.H. Wu, L.X. Su, X.H. Wang, Z.T. Liu, A novel non-destructive testing method by measuring the change rate of magnetic flux leakage. J. Nondest. Eval. 2(36), 24 (2017)Google Scholar
  10. 10.
    X.C. Liu, Y.J. Wang, B. Wu, Z. Gao, Design of tunnel magnetoresistive-based circular MFL sensor array for the detection of flaws in steel wire rope. J. Sens. 2016, 1–8 (2016)Google Scholar
  11. 11.
    C.C. Guo, Y.M. Wen, P. Li, W. Jin, Adaptive noise cancellation based on EMD in water-supply pipeline leak detection. Measurement 79, 188–197 (2016)CrossRefGoogle Scholar
  12. 12.
    X.J. Tan, J.W. Zhang, Evaluation of composite wire ropes using unsaturated magnetic excitation and reconstruction image with super-resolution. Appl. Sci. 5(8), 767 (2018)CrossRefGoogle Scholar
  13. 13.
    Z.Q. Wang, The Image Information Detecting System of Elevator Wire Rope. M.S. thesis, Donghua University, 2011Google Scholar
  14. 14.
    H.N. Ho, K.D. Kim, Y.S. Park, J.J. Li, An efficient image-based damage detection for cable surface in cable-stayed bridges. Ndt & E Int. 3(58), 18–23 (2013)CrossRefGoogle Scholar
  15. 15.
    Y.Y. Zhu, J.H. Zuo, J.P. Lu, D.X. Xu, A on-line detection system development based on image processing for rubber hose defects. Trans. Beijing Inst. Technol. 9(37), 937–941 (2017)Google Scholar
  16. 16.
    Y.Z. Zhang, L. Xu, L. Ding, J. Cao, Defects segmentation for wood floor based on image fusion method. Electric Mach. Control 7(18), 113–118 (2014)Google Scholar
  17. 17.
    W.W. Liu, Y.H. Yan, Z.Y. Li, Image filtering algorithm for online detection system of steel strip surface defects. J. Northeastern Univ. 3(30), 430–433 (2009)Google Scholar
  18. 18.
    J. Yu, The study on multi-focus textile fiber images fusion. M.S. thesis, Donghua University, 2011.Google Scholar
  19. 19.
    F. Liu, T.S. Shen, S.J. Guo, J. Zhang, Multi-spectral ship target recognition based on feature level fusion. Spectrosc. Spectr. Anal. 6(37), 1934–1940 (2017)Google Scholar
  20. 20.
    K.J. Wang, H. Ma, X.F. Li, Research on dual-modal second-level decision fusion for fingerprint and finger vein recognition. Control Decis. 8(26), 1131–1135 (2011)Google Scholar
  21. 21.
    J.N. Liu, W.Q. Jin, L. Li, X.L. Wang, Visible and infrared thermal image fusion algorithm based on self-adaptive reference image. Spectrosc Spectr Anal 36, 3907–3914 (2016)Google Scholar
  22. 22.
    J. Liu, L.S. Zhang, K.X. Xu, Multimodal face recognition based on images fusion on feature and decision levels. Nanotechnol. Precis. Eng. 1(1), 718–722 (2009)Google Scholar
  23. 23.
    M.J. Li, X.L. Wang, Y.B. Dong, Research and development of non multi-scale to pixel-level image fusion. Appl. Mech. Mater. 448–453 (2013)Google Scholar
  24. 24.
    C. Wu, J. Zhan, J. Jin, Nighttime images fusion based on Laplacian pyramid. Multispectr. Image Acquis. Process. Anal. 2018.Google Scholar
  25. 25.
    K.K. Sharma, N. Saxen, Hilbert vibration decomposition based image fusion. Electron. Lett. 52, 1605–1607 (2016)CrossRefGoogle Scholar
  26. 26.
    G. Sang, Y. Cai, H. Jing, A fractional Fourier transform based method of image fusion, in International Congress on Image & Signal Processing, IEEE 2013Google Scholar
  27. 27.
    Y. Ben-Shoshan, Y. Yitzhaky, Improvements of image fusion methods. J. Electron. Imaging 2 (No, 23) (2014)Google Scholar
  28. 28.
    W.X. Zhan, Research on signal process and quantitative recognition method of broken wires in wire rope. Ph.D. thesis, Qingdao University of Technological (2013)Google Scholar
  29. 29.
    M. Elad, M.A.T. Figueiredo, Y. Ma, On the role of sparse and redundant representations in image processing. Proc. IEEE 6(98), 972–982 (2010)CrossRefGoogle Scholar
  30. 30.
    S.J. Wright, R.D. Nowak, M.A.T. Figueiredo, Sparse reconstruction by separable approximation. IEEE Trans. Signal Process. 7(57), 2479–2493 (2009)CrossRefGoogle Scholar
  31. 31.
    T.C. Lan, B.H. Liu, Research of segmentation algorithms of steel wire rope image based on the spatial filtering and the spatial correlation. J. Fuzhou Univ. 5(36), 668–672 (2008)Google Scholar
  32. 32.
    L.W. Chen, C.R. Li, Invariant Moment Features for Fingerprint Recognition (IEEE Computer Society, Washington, 2013), pp. 92–94Google Scholar

Copyright information

© ASM International 2019

Authors and Affiliations

  1. 1.Electrical Engineering CollegeHenan University of Science and TechnologyLuoyangChina

Personalised recommendations