Skip to main content
Log in

Magnetic Flux Leakage Signal Inversion Based on Improved Efficient Population Utilization Strategy for Particle Swarm Optimization

  • Magnetic Methods
  • Published:
Russian Journal of Nondestructive Testing Aims and scope Submit manuscript

Abstract

In this paper, an improved efficient population utilization strategy for particle swarm optimization (IEPUS-PSO) for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL) signals. In the IEPUS-PSO, a mutation probability is proposed to distinguish local version and global version in particle change model and a self-adapted mutation operator, which is used to update the particles’ positions randomly, is introduced into EPUS-PSO. The IEPUS-PSO- based inversing technique is used to estimate the defect profiles. The estimated defect profiles of simulation signals demonstrate that the inversing technique based on the IEPUS-PSO outperforms the one based on EPUS-PSO. The results estimated from real MFL signals by the IEPUS-PSO-based inversing technique indicate that the algorithm is capable of decreasing the computation time. The results show that the IEPUS-PSO-based inversing technique could improve the reconstruction precision by two orders of magnitude for the MFL simulation signals, and for the real MFL signals, the computation time is reduced by about 30% nearly under the same reconstruction precision.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Zhongli Ma and Hongda Liu, Pipeline defect detection and sizing based on MFL data using immune RBF neural networks, CEC 2007, IEEE Congress, 2007, pp. 3399–3403.

    Chapter  Google Scholar 

  2. Chen, L., Peiwen Que, and Jin, T., A giant-magnetoresistance sensor for magnetic-flux-leakage nondestructive testing of a pipeline, Russ. J. Nondestr. Test., 2005, vol. 41, no. 7, pp. 462–465.

    Article  Google Scholar 

  3. Xiaochun Song, Song-Ling Huang, and Zhao Wei, Optimization of the magnetic circuit in the MFL inspection system for storage-tank floors, Russ. J. Nondestr. Test., 2007, vol. 43, no. 5, pp. 326–331.

    Article  Google Scholar 

  4. Kim, J.W., Lee, C., Park, S., and Lee, J.J., Magnetic flux leakage-based steel cable NDE and damage visualization on a cable climbing robot, Proc. SPIE Int. Soc. Opt. Eng., 2012, vol. 8345.

  5. Christen, R., Bergamini, A., and Motavalli, M., Influence of steel wrapping on magneto-inductive testing of the main cables of suspension bridges, NDT E Int., 2009, vol. 42, no. 1, pp. 22–27.

    Article  Google Scholar 

  6. Ramuhalli, P., Udpa, L., and Udpa, S.S., Electromagnetic NDE signal inversion by function-approximation neural networks, IEEE Trans. Magnet., 2002, vol. 38, no. 6, pp. 3633–3642.

    Article  Google Scholar 

  7. Chao Xu, Changlong Wang, Fengzhu Ji, and Xichao Yuan, Finite-element neural network-based solving 3-d differential equations in MFL, IEEE Trans. Magnet., 2012, vol. 48, no. 12, pp. 4747–4756.

    Article  Google Scholar 

  8. Dutta, S.M., Ghorbel, F.H., and Stanley, R.K., Dipole modeling of magnetic flux leakage, IEEE Trans. Magnet., 2009, vol. 45, no. 4, pp. 1959–1965.

    Article  Google Scholar 

  9. Dutta, S.M., Ghorbel, F.H., and Stanley, R.K., Simulation and analysis of 3-D magnetic flux leakage, IEEE Trans. Magnet., 2009, vol. 45, no. 4, pp. 1966–1972.

    Article  Google Scholar 

  10. Zhang, Y., Ye, Z., and Wang, C., A fast method for rectangular crack sizes reconstruction in magnetic flux leakage testing, NDT Int., 2009, vol. 42, no. 5, pp. 369–375.

    Article  Google Scholar 

  11. Priewald, R.H., Magele, C., Ledger, P.D., Pearson, N.R., and Mason, J.S.D., Fast magnetic flux leakage signal inversion for the reconstruction of arbitrary defect profiles in steel using finite elements, IEEE Trans. Magnet., 2013, vol. 49, no. 1.

    Google Scholar 

  12. Guoguang, Z. and Jing, L., Finite element modeling of circumferential magnetic flux leakage inspection in pipeline, Proc. Int. Conf. Intelligent Computation Technology and Automation (ICICTA’10) (Changsha, China, 2010), pp. 327–330.

    Google Scholar 

  13. Song Qiang, Interacting effects of clustering defects on MFL signals using FEA, Insight, 2013, vol. 55, pp. 558–560.

    Article  Google Scholar 

  14. Chao Xu, Changlong Wang, Fengzhu Ji, and Xichao Yuan, Finite-element neural network-based solving 3-D differential equations in MFL, IEEE Trans. Magnet., 2012, vol. 48, no.12.

    Google Scholar 

  15. Joshi, A., Udpa, L., Udpa, S., and Tamburrino, A., Adaptive wavelets for characterizing magnetic flux leakage signals from pipeline inspection, IEEE Trans. Magnet., 2006, vol. 42, no.10.

    Google Scholar 

  16. Hari, K.C., Nabi, M., and Kulkarni, S.V., Improved fem model for defect-shape construction from MFL signal by using genetic algorithm, IET Sci. Meas. Technol., 2007, vol. 1, no. 4, pp. 196–200.

    Article  Google Scholar 

  17. WenHua Han, Ping Yang, Fei Xia, and Yang Xue, Magnetic flux leakage signal inversion of corrosive flaws based on modified genetic local search algorithm, J. Shanghai Jiaotong Univ. (Sci.), 2009, vol. 14 (E), no. 2, pp. 168–172.

    Article  Google Scholar 

  18. Eberhart, R.C. and Kennedy, J., A new optimizer using particle swarm theory, Proc. 6th Int. Symp. Micro Mach. Human Sci. (Nagoya, Japan, 1995), pp. 39–43.

    Chapter  Google Scholar 

  19. van den Bergh, F. and Engelbrecht, A.P., A cooperative approach to particle swarm optimization, IEEE Trans. Evol. Comput., 2004, vol. 8, no. 3, pp. 225–239.

    Article  Google Scholar 

  20. Hsieh, S.T., Sun, T.Y., Liu, C.C., and Tsai, S.J., Efficient population utilization strategy for particle swarm optimizer, IEEE Trans. System, Man, Cybernetics B: Cybernetics, 2009, vol. 39, no. 2.

    Google Scholar 

  21. LiJue Liu, ZiXing Cai, and Hong Chen, Immunity clone algorithm with particle swarm evolution, J. Central South Univ. Technol., 2006, vol. 13, no. 6, pp. 703–706.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wenhua Han.

Additional information

Published in Russian in Defektoskopiya, 2017, No. 12, pp. 46–56.

The article was translated by the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, W., Wu, Z., Zhou, M. et al. Magnetic Flux Leakage Signal Inversion Based on Improved Efficient Population Utilization Strategy for Particle Swarm Optimization. Russ J Nondestruct Test 53, 862–873 (2017). https://doi.org/10.1134/S1061830917120075

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1061830917120075

Keywords

Navigation