Skip to main content
SpringerLink
Log in

Defect Width Estimation of Magnetic Flux Leakage Signal with Wavelet Scattering Transform

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

The magnetic flux leakage (MFL) technique is widely employed for nondestructive testing of ferromagnetic specimens and materials, including wire ropes, bridge cables, and pipelines. As regards the MFL testing, extracting features from MFL signals is crucial for defect recognition and estimation of corresponding widths. Deep learning has been extensively used for feature extraction, but it often performs inadequately on a small sample dataset. To address this limitation, this paper develops a network framework that combines the Wavelet Scattering Transform (WST) and Neural Networks (NN) for defect width estimation. The WST is a knowledge-based feature extraction technique with a structure similar to convolutional neural networks. It offers a translation-invariant representation of signal features using a redundant dictionary of wavelets. The NN then maps the WST feature representation to the defect width information. Experiments on real steel plates with defects are carried out to validate the effectiveness of the proposed framework. Quantitative comparisons of the experimental results demonstrate that the proposed framework achieves better estimation performance in handling MFL signals and has superiority in scenarios with limited training samples.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data Availibility Statement

The data in the research are obtained and analyzed based on experimental measurements.

References

  1. Bubenik, T., Nestlroth, J., Eiber, R., Saffell, B.: Magnetic flux leakage (mfl) technology for natural gas pipeline inspection. NDT E Int. 1(30), 36 (1997)

    Google Scholar 

  2. Dutta, S.M., Ghorbel, F.H., Stanley, R.K.: Dipole modeling of magnetic flux leakage. IEEE Trans. Magnet. 45(4), 1959–1965 (2009)

    Article  Google Scholar 

  3. Shi, Y., Zhang, C., Li, R., Cai, M., Jia, G.: Theory and application of magnetic flux leakage pipeline detection. Sensors 15(12), 31036–31055 (2015)

    Article  Google Scholar 

  4. Peng, X., Anyaoha, U., Liu, Z., Tsukada, K.: Analysis of magnetic-flux leakage (mfl) data for pipeline corrosion assessment. IEEE Trans. Magnet. 56(6), 1–15 (2020)

    Article  Google Scholar 

  5. Jin, Z., Mohd Noor Sam, M.A.I., Oogane, M., Ando, Y.: Serial mtj-based tmr sensors in bridge configuration for detection of fractured steel bar in magnetic flux leakage testing. Sensors 21(2), 668 (2021)

    Article  Google Scholar 

  6. Long, Y., Huang, S., Peng, L., Wang, S., Zhao, W.: A characteristic approximation approach to defect opening profile recognition in magnetic flux leakage detection. IEEE Trans. Instrum. Meas. 70, 1–12 (2021)

    Google Scholar 

  7. Li, F., Feng, J., Zhang, H., Liu, J., Lu, S., Ma, D.: Quick reconstruction of arbitrary pipeline defect profiles from mfl measurements employing modified harmony search algorithm. IEEE Trans. Instrum. Meas. 67(9), 2200–2213 (2018)

    Article  Google Scholar 

  8. Huang, S., Peng, L., Wang, Q., Wang, S., Zhao, W.: An opening profile recognition method for magnetic flux leakage signals of defect. IEEE Trans. Instrum. Meas. 68(6), 2229–2236 (2018)

    Article  Google Scholar 

  9. Piao, G., Guo, J., Hu, T., Leung, H., Deng, Y.: Fast reconstruction of 3-d defect profile from mfl signals using key physics-based parameters and svm. NDT & E Int. 103, 26–38 (2019)

    Article  Google Scholar 

  10. Yu, G., Liu, J., Zhang, H., Liu, C.: An iterative stacking method for pipeline defect inversion with complex mfl signals. IEEE Trans. Instrum. Meas. 69(6), 3780–3788 (2019)

    Article  Google Scholar 

  11. Zhang, H., Wang, L., Wang, J., Zuo, F., Wang, J., Liu, J.: A pipeline defect inversion method with erratic mfl signals based on cascading abstract features. IEEE Trans. Instrum. Meas. 71, 1–11 (2022)

    Google Scholar 

  12. Han, W., Xu, J., Wang, P., Tian, G.: Defect profile estimation from magnetic flux leakage signal via efficient managing particle swarm optimization. Sensors 14(6), 10361–10380 (2014)

    Article  Google Scholar 

  13. Lu, S., Feng, J., Zhang, H., Liu, J., Wu, Z.: An estimation method of defect size from mfl image using visual transformation convolutional neural network. IEEE Trans. Indust. Inf. 15(1), 213–224 (2018)

    Article  Google Scholar 

  14. Wang, L., Zhang, H., Liu, J., Qu, F., Zuo, F.: Defect size quantification for pipeline magnetic flux leakage detection system via multi-level knowledge-guided neural network. IEEE Trans. Indust. Elect. (2022)

  15. Wu, Z., Deng, Y., Liu, J., Wang, L.: A reinforcement learning-based reconstruction method for complex defect profiles in mfl inspection. IEEE Trans. Instrum. Meas. 70, 1–10 (2021)

    Google Scholar 

  16. Bruna, J., Mallat, S.: Invariant scattering convolution networks. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1872–1886 (2013)

    Article  Google Scholar 

  17. Mallat, S.: Group invariant scattering. Commun. Pure Appl. Math. 65(10), 1331–1398 (2012)

    Article  MathSciNet  Google Scholar 

  18. Andén, J., Mallat, S.: Deep scattering spectrum. IEEE Trans. Signal Processing 62(16), 4114–4128 (2014)

    Article  MathSciNet  Google Scholar 

  19. Oyallon, E., Mallat, S., Sifre, L.: Generic deep networks with wavelet scattering. arXiv preprint arXiv:1312.5940 (2013)

  20. Edwards, C., Palmer, S.: The magnetic leakage field of surface-breaking cracks. J. Phys. Appl. Phys. 19(4), 657 (1986)

    Article  Google Scholar 

  21. Sifre, L., Mallat, S.: Rotation, scaling and deformation invariant scattering for texture discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1233–1240 (2013)

  22. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

Download references

Acknowledgements

This work is partially supported by National Key R &D Program of China (2021YFE0204200), the Guangdong Basic and Applied Basic Research Foundation under Grant No.2019A1515111119 and No.2021A1515010926, and Shenzhen Science and Technology Program under Grant No.JSGG20210802154539015.

Author information

Author notes

  1. Zehao Fang and Min Zhao have contributed equally to this work.

Authors and Affiliations

  1. Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, 518129, Shenzhen, Guangdong, China

    Zehao Fang, Min Zhao, Huihuan Qian, Ning Ding & Nan Li

  2. School of Science and Engineering, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China

    Zehao Fang & Huihuan Qian

  3. Institute of Robotics and the Intelligent Manufacturing, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China

    Min Zhao, Huihuan Qian, Ning Ding & Nan Li

Authors
  1. Zehao Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huihuan Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ning Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZF: Methodology, Analysis and interpretation of data, Software, Writing-Review & Editing. MZ: Conceptualization, Data curation, Validation, Writing-Review & Editing. HQ: Supervision, Writing-Review & Editing. ND: Project administration, Supervision. NL: Validation, Writing-Review & Editing.

Corresponding authors

Correspondence to Min Zhao or Huihuan Qian.

Ethics declarations

Conflicts of interest

The authors declare that there is no Conflict of interest.

Ethics Approval

This paper has not been submitted simultaneously to any other journal. The paper has not been published previously. All the authors have reviewed the final version of the manuscript and consent to participate in the publication.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, Z., Zhao, M., Qian, H. et al. Defect Width Estimation of Magnetic Flux Leakage Signal with Wavelet Scattering Transform. J Nondestruct Eval 43, 46 (2024). https://doi.org/10.1007/s10921-024-01061-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10921-024-01061-0

Keywords

Search

Navigation