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

Lookup NU author(s): Professor Gui Yun TianORCiD

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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.

Publication metadata

Author(s): Han WH, Xu J, Zhou MC, Tian GY, Wang P, Shen XH, Hou E

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Magnetics

Year: 2016

Volume: 52

Issue: 4

Print publication date: 01/04/2016

Online publication date: 05/11/2015

Acceptance date: 01/01/1900

ISSN (print): 0018-9464

ISSN (electronic): 1941-0069

Publisher: Institute of Electrical and Electronics Engineers

URL: http://dx.doi.org/10.1109/TMAG.2015.2498119

DOI: 10.1109/TMAG.2015.2498119

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