Art

J-GLOBAL ID：201702277134726622   Reference number：17A0356516

Magnetic Flux Leakage Quantitative Identification of Micro Crack Based on GA-BP Neural Network

GA-BPニューラルネットワークに基づく微裂紋漏磁の定量的認識技術【JST・京大機械翻訳】

Author (5)： Qiu Zhongchao (School of Mechanical Engineering,Beijing Institute of Technology, Advanced Manufacture Technology Center,China Academy of Machinery Science&Technology) ,  Zhang Weimin (School of Mechanical Engineering,Beijing Institute of Technology) ,  Zhang Ruilei (Hebei Aerospace Environmental Engineering Co.,Ltd) ,  Yu Xia (Navigation and Control Technology Research Institute,China North Industries Group Corporation) ,  Chen Guolong (School of Mechanical Engineering,Beijing Institute of Technology)
Material： Beijing Ligong Daxue Xuebao  (Pekin Riko Daigaku Gakuho)
Volume： 36  Issue： 11  Page： 1101-1104,1121  Publication year： 2016 
JST Material Number： W0652A  ISSN： 1001-0645  CODEN： BLXUEV  Document type： Article
Article type： 原著論文  Country of issue： China (CHN)  Language： CHINESE (ZH)

Abstract/Point： As the fact that the crack siz...

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Thesaurus term： noise (signal) ,  optimization ,  magnetic dipole ,  *neural network (biomodel) ,  algorithm ,  crack ,  interference ,  genetic algorithm ,  microcrack ,  theoretical calculation ,  *back propagation neural network ,  modification ,  evolutionary computation

Author keywords (6)： magnetic flux leakage ,  genetic algorithm(GA) ,  back propagation(BP) ,  neural network ,  micro crack ,  quantitative identification

JST classification (1)： Nondestructive testing  (HB02030F)

Terms in the title (1)： 定量