中国石油大学学报（自然科学版）2016，Vol.40Issue(5)：128-134,7.DOI:10.3969/j.issn.1673-5005.2016.05.016
基于GA-BP神经网络的ACFM实时高精度裂纹反演算法
Real-time and high-precision cracks inversion algorithm for ACFM based on GA-BP neural network
摘要
Abstract
It is hard to achieve a real-time and high-precision cracks inversion for alternating current field measurement (ACFM) based on traditional characteristic signals. In this paper, based on the finite element method (FEM) model of e-lectromagnetic coupling ACFM probe, the energy spectrum and phase threshold determination methods were presented to ob-tain the crack characteristic signals in real time. The real-time and high-precision cracks inversion system for ACFM was set up and verified by artificial cracks experiment. The length and depth of cracks were calculated using the characteristic signals obtained from experiments based on the genetic algorithm and back propagation neural network( GA-BP) real-time and high-precision cracks inversion algorithm. The results show that the FEM model of electromagnetic coupling ACFM probe can sim-ulate the characteristic signals perfectively, the energy spectrum and phase threshold determination method can obtain the crack characteristic signals in real time, the GA-BP neural network can realize the inversion of the length and depth of crack perfectly and the relative error of inversion accuracy is less than 10%.关键词
ACFM/实时/高精度/裂纹反演算法/遗传算法/BP神经网络Key words
alternating current field measurement(ACFM)/real-time/high-precision/cracks inversion algorithm/genetic algorithm/BP neural network分类
数理科学引用本文复制引用
李伟,袁新安,曲萌,陈国明,葛玖浩,孔庆晓,张雨田,吴衍运..基于GA-BP神经网络的ACFM实时高精度裂纹反演算法[J].中国石油大学学报（自然科学版）,2016,40(5):128-134,7.基金项目
国家自然科学基金项目(51574276) （51574276）
中央高校基本科研业务费专项(15CX05024A) （15CX05024A）
山东省自然科学基金英才基金项目(ZR2015EM009) （ZR2015EM009）
青岛市科技成果转化引导计划(青年专项)(14-2-4-49-jch) （青年专项）
中国石油大学(华东)研究生创新工程(YCX2015039) （华东）
