Skip to main content

Advertisement

SpringerLink
Log in

Pipe Defect Detection with Remote Magnetic Inspection and Wavelet Analysis

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

High-pressure pipeline has significant magneto-mechanical effect in the geomagnetic field. Combined with magnetic inspection technology on the ground, the magnetic anomalies induced by buried pipeline defects can be detected and then be analyzed. But in remote inspection condition, the signal to noise ratio of magnetic signal is too low, and the pipeline magnetic anomalies will be obscured in the complex magnetic background. The difficulty for non-excavation pipeline detection is how to separate its magnetic anomalies from remote magnetic signals. In this paper, a nonlinear structural finite element analysis model was created for pipeline magnetic testing and the properties of three components of the magnetic field are investigated. The Φ220 mm × 10 mm pipe was detected under the conditions of 8 MPa pipe pressure and 2 m inspection distance, and a signal processing method based on wavelet analysis was also proposed. Experiment results showed that the magnetic anomalies induced by pipe cracks with different depth could be recognized.

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

Similar content being viewed by others

References

  1. Li, C. J., Chen, C., & Liao, K. X. (2015). A quantitative study of signal characteristics of non-contact pipeline magnetic testing. Insight, 57, 324–330.

    Article  Google Scholar 

  2. Dubov, A., & Kolokolnikov, S. (2012). Assessment of the material state of oil and gas pipelines based on the metal magnetic memory method. Welding in the World, 56, 11–19.

    Article  Google Scholar 

  3. Cawley, P., Lowe, M., Alleyne, D., Pavlakovic, B., & Wilcox, P. (2003). Practical long range guided wave testing: Applications to pipes and rail. Materials Evaluation, 61, 66–74.

    Google Scholar 

  4. Jarvis, R., Cawley, P., & Nagy, P. B. (2016). Current deflection NDE for the inspection and monitoring of pipes. NDT & E International, 81, 46–59.

    Article  Google Scholar 

  5. Krivoi, G. (2008). NoPig: An above-ground inspection technique for non-piggable pipelines. Oil Gas-European Magazine, 34, 122–124.

    Google Scholar 

  6. Jiles, D. C. (1995). Theory of the magnetomechanical effect. Journal of Physics D-Applied Physics, 28, 1537–1546.

    Article  Google Scholar 

  7. Dubov, A. (1997). A study of metal properties using the method of magnetic memory. Metal Science and Heat Treatment, 39, 401–405.

    Article  Google Scholar 

  8. Li, J., Xu, M., & Leng, J. (2011). Metal magnetic memory effect caused by circle tensile-compressive stress. Insight, 53, 142–145.

    Article  Google Scholar 

  9. RD 102-008-2009. (2002). Application guide of the non-contact pipeline magnetic testing, The Russian Federation Ore Industry Committee.

  10. Roskosz, M., & Bieniek, M. (2012). Evaluation of residual stress in ferromagnetic steels based on residual magnetic field measurements. NDT & E International, 45, 55–62.

    Article  MATH  Google Scholar 

  11. Wang, Z. D., Yao, K., & Deng, B. (2010). Quantitative study of metal magnetic memory signal versus local stress concentration. NDT & E International, 43, 513–518.

    Article  Google Scholar 

  12. Liao, K. X., Yao, Q., & Zhang, C. (2011). Principle and technical characteristics of non-contact magnetic tomography method inspection for oil and gas pipeline. In Proceedings of the international conference on pipelines and trenchless technology, pp. 1039–1048.

  13. Jiles, D. C., & Sablik, M. J. (1993). Coupled magnetoelastic theory of magnetic and magnetostrictive hysteresis. IEEE Transactions on Magnetics, 29, 2113–2123.

    Article  Google Scholar 

  14. Song, Q. (2013). Interacting effects of clustering defects on MFL signals using FEA. Insight, 55, 558–560.

    Article  Google Scholar 

  15. Huang, H., Yang, C., Qian, Z., Han, G., & Liu, Z. (2016). Magnetic memory signals variation induced by applied magnetic field and static tensile stress in ferromagnetic steel. Journal of Magnetism and Magnetic Materials, 416, 213–219.

    Article  Google Scholar 

  16. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transaction on Pattern Analysis and Machine Intelligence, 11, 674–693.

    Article  MATH  Google Scholar 

  17. Ni, S., Huang, Y., & Lo, K. (2010). Buried pipe detection by ground penetrating radar using the discrete wavelet transform. Computers and Geotechnics, 37, 440–448.

    Article  Google Scholar 

  18. Wu, J., & Kuo, J. (2009). An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network. Expert Systems with Applications, 36, 9776–9783.

    Article  Google Scholar 

  19. Yin, G., Zhang, Y., Fan, H., & Li, Z. (2014). Magnetic dipole localization based on magnetic gradient tensor data at a single point. Journal of Applied Remote Sensing, 8, 1–18.

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank National Key Technology R&D Program (2015BAK16B02), China National Key Research and Development Plan (2016YFC0303700), Science Foundation of China University of Petroleum, Beijing (2462015YQ0405 and C201602), and Science Foundation of Karamay campus of China University of Petroleum-Beijing (RCYJ2016B-02-007) for financial support.

Author information

Authors and Affiliations

  1. College of Mechanical and Transportation Engineering, China University of Petroleum, 18 Fuxue Road, Changping, Beijing, 102249, China

    Qiang Song, He Peng, Jijun Gu & Jian Shuai

  2. Faulty of Engineering, Karamay Campus of China University of Petroleum-Beijing, 355 Anding Road, Karamay, 834000, Xinjiang, China

    Qiang Song

  3. Beijing Nankou SKF Railway Bearings Co., Ltd., Nankou Town, Changping, Beijing, 102202, China

    Weixia Ding

Authors
  1. Qiang Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weixia Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. He Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jijun Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jian Shuai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiang Song.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, Q., Ding, W., Peng, H. et al. Pipe Defect Detection with Remote Magnetic Inspection and Wavelet Analysis. Wireless Pers Commun 95, 2299–2313 (2017). https://doi.org/10.1007/s11277-017-4092-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4092-8

Keywords

Search

Navigation