空军工程大学学报（自然科学版）2018，Vol.19Issue(1)：72-78,7.DOI:10.3969/j.issn.1009-3516.2018.01.013
基于堆叠自编码器神经网络的复合电磁检测铁磁性双层套管腐蚀缺陷分类识别方法
Classification of Subsurface Corrosion in Double-casing Pipes via Integrated Electromagnetic Testing with Stacked Auto-encoder Artificial Neural Network
摘要
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.关键词
亚表面腐蚀缺陷/分类识别/铁磁性双层套管/脉冲远场涡流检测/脉冲涡流检测/堆叠自编码器神经网络Key words
subsurface corrosion/defect classification/ferromagnetic double-casing pipe/pulsed remote field eddy current/pulsed eddy current/stacked auto-encoder artificial neural network分类
矿业与冶金引用本文复制引用
张曦郁,李勇,闫贝,敬好青..基于堆叠自编码器神经网络的复合电磁检测铁磁性双层套管腐蚀缺陷分类识别方法[J].空军工程大学学报（自然科学版）,2018,19(1):72-78,7.基金项目
国家自然科学基金(51477127 （）
E070104) （）
