基于迁移学习的GH159螺栓热镦后头部缺陷识别  

Head Defect Recognition of GH159 Bolt After Hot Upsetting Based on Transfer Learning

作　　者：黎磊 马钰淋 胡刚[2] 孔雪峰 杨军[1] 许彦伟 LI Lei;MA Yulin;HU Gang;KONG Xuefeng;YANG Jun;XU Yanwei(School of Reliability and Systems Engineering,Beihang University,Beijing 100191;No.722 Research Institute of China State Shipbuilding Corporation Limited,Wuhan 430205;Aerospace Precision Industry Corporation Limited,Tianjin 300300)

机构地区：[1]北京航空航天大学可靠性与系统工程学院,北京100191 [2]中国船舶重工集团公司第七二二研究所,武汉430205 [3]航天精工股份有限公司,天津300300

出　　处：《系统科学与数学》2022年第1期175-192,共18页Journal of Systems Science and Mathematical Sciences

基　　金：技术基础科研项目(JSZL2019204B007)资助课题。

摘　　要：为准确进行GH159螺栓热镦后头部缺陷识别,提出了基于迁移学习的缺陷识别方法,其中,不同场景亮度下的数据集分别设置为迁移学习的源域,目标域.首先,考虑域条件分布的多簇特点,使用K-means算法对同类缺陷数据进行簇划分,确定簇中心,并基于其构造新的分布差异度量;其次,为有效提升迁移学习计算效率,使用簇中心间距离以及各簇中心与该簇样本间距离,建立新的类内差异度量;最后,以分布差异度量与类内差异度量的加权和最小化为目标,准确识别不同场景亮度下的缺陷.针对所提出方法的参数设定需求,基于反向验证理念设计伪精度,并以其最大化进行参数确定.基于收集的GH159螺栓热镦后头部缺陷数据集,开展缺陷识别分析应用,验证所提出方法的有效性.To accurately identify the head defects of the GH159 bolt after hot upsetting,this paper proposes a defect recognition method based on transfer learning,where datasets under scenes with different brightness are set as the source domain and target domain in transfer learning,respectively.First,considering the multi clusters of the conditional distribution in the domain,this paper adopts the K-means algorithm to cluster samples with the same defect and determine the cluster centers in this defect,then a novel measurement of the distribution discrepancy can be constructed on the cluster centers.Second,based on the distances between cluster centers and the distances between each cluster center and the samples belonging to the cluster,a new intra-class discrepancy can be established for improving the computational efficiency of transfer learning.Finally,the optimization objective of the proposed method is built on minimizing the weighted sum of the constructed distribution discrepancy and intra-class discrepancy to effectively identify defects under scenes with different brightness.According to the requirement on partial parameters setting of the proposed method,the pseudo-accuracy is designed using the reverse verification strategy,then the parameters are set as the parameters’ combination with the highest pseudo-accuracy.Using the collected dataset on head defects of the GH159 bolt after hot upsetting,the analysis and application of the defect recognition are carried out to verify the effectiveness of the proposed method.

关 键 词：迁移学习 缺陷识别 簇中心 分布差异 类内差异 

分 类 号：V263[航空宇航科学与技术—航空宇航制造工程] V252[一般工业技术—材料科学与工程] TH131.3[机械工程—机械制造及自动化]