Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals

Wenhua Han, Jun Xu, Mengchu Zhou, Guiyun Tian, Ping Wang, Xiaohui Shen, Edwin Hou

Research output: Contribution to journalArticlepeer-review

70 Scopus citations

Abstract

Accurate and timely prediction of defect dimensions from magnetic flux leakage signals requires one to solve an inverse problem efficiently. This paper proposes a new inversing approach to such a problem. It combines cuckoo search (CS) and particle filter (PF) to estimate the defect profile from measured signals and adopts a radial-basis function neural network as a forward model as well as the observation equation in PF. As one of the latest nature-inspired heuristic optimization algorithms, CS can solve high-dimensional optimization problems. As an effective estimator for a nonlinear filtering problem, PF is applied to the proposed inversing approach in order to improve the latter's robustness to the noise. The resulting algorithm enjoys the advantages of both CS and PF where CS produces the optimized state sequence for PF while PF processes the state sequence and estimates the desired profile. The simulation and experimental results have demonstrated that the proposed approach is significantly better than the inversing approach based on CS alone in a noisy environment.

Original languageEnglish (US)
Article number7321028
JournalIEEE Transactions on Magnetics
Volume52
Issue number4
DOIs
StatePublished - Apr 2016

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

Keywords

  • Cuckoo search
  • inversing problem
  • magnetic flux leakage
  • particle filter

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