Electromagnetic Nondestructive Testing in Cracked Defects of Oil-Gas Casing Based on Ant Colony Neural Network

Abstract:

In this paper, a new method on quantitative analysis of magnetic flux leakage signal by ant colony neural network is proposed. Firstly, the parameters of the magnetic flux leakage signal which can reflect the various characteristics of cracked defects are determined by finite element method (FEM) simulation. Secondly, based on the study of the ant colony algorithm, the neural network model is established for the magnetic flux leakage signals processing. Finally, in the simulated working environment, the performance of the neural network is tested with the different signal features as input. The experimental results proved the feasibility of the ant colony neural network, verified the increases of the convergence rate and the accuracy of the neural network, and improved the efficiency as well as the quality of the quantitative analysis for the magnetic flux leakage signals.

• Periodical: Advanced Materials Research (Volumes 605-607)
• Pages: 760-763
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.605-607.760
• Online since: December 2012
• Authors: Wei Zhang, Yi Bing Shi, Yan Jun Li
• Keywords: Finite Element Method (FEM), Magnetic Coil Parameter, Magnetic Flux Leakage (MFL), Oil Casing Damage

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