Classification of Subsurface Corrosion in Double-casing Pipes via Integrated Electromagnetic Testing with Stacked Auto-encoder Artificial Neural Network
ZHANG Xiyu
LI Yong
YAN Bei
JING Haoqing
Abstract：The in-service Ferromagnetic Double-casing Pipe(FDP)is prone to Subsurface Corrosion(SSC) in the rigorous environments.It is necessary to evaluate SSC periodically.On the premise of defect classi-fication in quantitative evaluation and maintenance,the real-time classification of SSC is of great impor-tance.In light of this,this paper proposes a stacked Auto-Encoder Artificial Neural Network(SAE-ANN) classification method for classification of SSC in FDP in conjunction with Pulsed Remote Field Eddy Cur-rent(PRFEC)and Pulsed Eddy Current(PEC).By choosing appropriate eigenvalue as the input layer,3 SSC scenarios(corrosion on external surfaces of inner and outer casing pipes;corrosion on the internal surface of the outer casing pipe)can be identified.The accuracy can reach 97.5% and the result shows that the proposed method is capable of identifying the localized SCC without much loss in accuracy.
Keywords：subsurface corrosiondefect classificationferromagnetic double-casing pipepulsed remote field eddy currentpulsed eddy currentstacked auto-encoder artificial neural network
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 72-78 )