Skip to main content

Quantitative Detection of Remanence in Broken Wire Rope Based on Adaptive Filtering and Elman Neural Network

Abstract

In recent years, non-destructive testing methods for wire ropes based on remanence have attracted industry attention. The remanence detection methods have the characteristics of light equipment, high lifting value, high detection precision and low requirements on site conditions. An adaptive filtering algorithm based on wavelet decomposition was proposed to deal with the noise reduction of broken wire rope remanence data. The digital image processing method was used to locate and segment the defect. The texture features, morphological features and seventh-order invariant moments of the defect image were extracted as feature vectors, and an Elman neural network was designed to quantitatively identify the broken wires. The experimental results show that the designed filtering algorithm can effectively suppress the noise in the original signal, and the Elman recognition network has better performance of broken wire recognition.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  1. S. Yang, Y. Kang, Electromagnetic Nondestructive Testing of Wire Ropes (Mechanical Industry Press, Beijing, 2016)

    Google Scholar 

  2. J. Tian, J. Zhou, H. Wang, G. Meng, Literature review of research on the technology of wire rope nondestructive inspection in China and abroad. MATEC Web Conf. 22, 03025 (2015). https://doi.org/10.1051/matecconf/20152203025

    Article  Google Scholar 

  3. S. Huang, Y. Sun, Modern Magnetic Flux Leakage Nondestructive Testing (Mechanical Industry Press, Beijing, 2016)

    Book  Google Scholar 

  4. G. Shen, B. Wang, Research and development status of magnetic flux leakage detection technology. Detect. Technol. 33(9), 43–52 (2017)

    Google Scholar 

  5. H. Wang, Z. Xu, G. Hua, J. Tian, B. Zhou, Y. Lu et al., Key technique of a detection sensor for coal mine wire ropes. Min. Sci. Technol. 19(2), 170–175 (2009)

    CAS  Article  Google Scholar 

  6. M. Zhao, Research on Key Technologies of Quantitative Detection of Magnetic Leakage in Local Defects (Harbin Institute of Technology, Harbin, 2012)

    Google Scholar 

  7. J. Wu, F. Hui, L. Long, K. Yihua, F. Kojima, F. Kobayashi et al., The signal characteristics of rectangular induction coil affected by sensor arrangement and scanning direction in MFL application. Int. J. Appl. Electromagn. Mech. 52(3–4), 1257–1265 (2016). https://doi.org/10.3233/jae-162151

    Article  Google Scholar 

  8. X. Yan, D. Zhang, F. Zhao, Improve the signal to noise ratio and installation convenience of the inductive coil for wire rope nondestructive testing. NDT E Int. 92, 221–227 (2017). https://doi.org/10.1016/j.ndteint.2017.09.005

    Article  Google Scholar 

  9. D. Wu, L. Su, X. Wang, Z. Liu, A novel non-destructive testing method by measuring the change rate of magnetic flux leakage. J. Nondestruct. Eval. (2017). https://doi.org/10.1007/s10921-017-0396-6

    Article  Google Scholar 

  10. F. Xu, X. Wang, H. Wu, Inspection method of cable-stayed bridge using magnetic flux leakage detection: principle, sensor design, and signal processing. J. Mech. Sci. Technol. 26(3), 661–669 (2012). https://doi.org/10.1007/s12206-011-1234-x

    Article  Google Scholar 

  11. X. Yan, D. Zhang, S. Pan, E. Zhang, W. Gao, Online nondestructive testing for fine steel wire rope in electromagnetic interference environment. NDT E Int. 92, 75–81 (2017). https://doi.org/10.1016/j.ndteint.2017.07.017

    CAS  Article  Google Scholar 

  12. W.S. Singh, B.P.C. Rao, S. Thirunavukkarasu, T. Jayakumar, Flexible GMR sensor array for magnetic flux leakage testing of steel track ropes. J. Sens. (2012). https://doi.org/10.1155/2012/129074

    Article  Google Scholar 

  13. Y. Cao, Research on Quantitative Detection of Local Defects of Steel WIRE rope Based on Magnetic Flux Leakage Imaging Principle (Harbin Institute of Technology, Harbin, 2007)

    Google Scholar 

  14. J. Zhang, X. Tan, Quantitative inspection of remanence of broken wire rope based on compressed sensing. Sensors (2016). https://doi.org/10.3390/s16091366

    Article  Google Scholar 

  15. J. Zhang, X. Tan, P. Zheng, Non-destructive detection of wire rope discontinuities from residual magnetic field images using the Hilbert-Huang transform and compressed sensing. Sensors (2017). https://doi.org/10.3390/s17030608

    Article  Google Scholar 

  16. J. Zhang, P. Zheng, X. Tan, Recognition of broken wire rope based on remanence using EEMD and wavelet methods. Sensors (2018). https://doi.org/10.3390/s18041110

    Article  Google Scholar 

  17. X. Tan, J. Zhang, Evaluation of composite wire ropes using unsaturated magnetic excitation and reconstruction image with super-resolution. Appl. Sci. 8(5), 767 (2018). https://doi.org/10.3390/app8050767

    Article  Google Scholar 

  18. Ingrid Daubechies, J. Li, Wavelet Ten Lectures (National Defence Industry Press, Beijing, 2011)

    Google Scholar 

  19. J. Li, L. Chang, A SAR image compression algorithm based on Mallat tower-type wavelet decomposition. Optik 126(23), 3982–3986 (2015). https://doi.org/10.1016/j.ijleo.2015.07.196

    Article  Google Scholar 

  20. W. Wang, H. Zhao, L. Lu, Y. Yu, Bias-compensated constrained least mean square adaptive filter algorithm for noisy input and its performance analysis. Digit. Signal Proc. 84, 26–37 (2019). https://doi.org/10.1016/j.dsp.2018.07.021

    Article  Google Scholar 

  21. J. Sang, H. Wang, Q. Qian, H. Wu, Y. Chen, An efficient fingerprint identification algorithm based on minutiae and invariant moment. Pers. Ubiquit. Comput. 22(1), 71–80 (2017). https://doi.org/10.1007/s00779-017-1094-1

    Article  Google Scholar 

  22. G. Ren, Y. Cao, S. Wen, T. Huang, Z. Zeng, A modified Elman neural network with a new learning rate scheme. Neurocomputing 286, 11–18 (2018). https://doi.org/10.1016/j.neucom.2018.01.046

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 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 information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to JuWei Zhang or ShiLiang Lu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Lu, S. & Gao, T. Quantitative Detection of Remanence in Broken Wire Rope Based on Adaptive Filtering and Elman Neural Network. J Fail. Anal. and Preven. 19, 1264–1274 (2019). https://doi.org/10.1007/s11668-019-00709-8

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11668-019-00709-8

Keywords

  • Non-destructive testing
  • Wavelet decomposition
  • Adaptive filtering
  • Morphological features
  • Elman neural network