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

Wenhua Han, Zhengyang Wu, Mengchu Zhou, Edwin Hou, Xiaoyan Su, Ping Wang, Guiyun Tian

Research output: Contribution to journalComment/debatepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)862-873
Number of pages12
JournalRussian Journal of Nondestructive Testing
Volume53
Issue number12
DOIs
StatePublished - Dec 1 2017

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

Keywords

  • inverse algorithm
  • magnetic flux leakage
  • profile estimation
  • signal inversion

Fingerprint Dive into the research topics of 'Magnetic Flux Leakage Signal Inversion Based on Improved Efficient Population Utilization Strategy for Particle Swarm Optimization'. Together they form a unique fingerprint.

Cite this