基于SHAP可解释性的焊缝缺陷类型超声识别XGBoost模型  

XGBoost model for ultrasonic recognition of weld defect types based on SHAP interpretability

作　　者：陈明良 马志远[1] 张东辉 付冬欣 廖静瑜 林莉[1] CHEN Mingliang;MA Zhiyuan;ZHANG Donghui;FU Dongxin;LIAO Jingyu;LIN Li(NDT&E Laboratory,Dalian University of Technology,Dalian 116024,China;China Nuclear Industry 23 Construction Co.,Ltd.,Beijing 101300,China)

机构地区：[1]大连理工大学无损检测研究所,大连116024 [2]中国核工业二三建设有限公司,北京101300

出　　处：《无损检测》2024年第6期36-42,共7页Nondestructive Testing

摘　　要：针对焊缝缺陷机器学习超声识别过程中存在特征冗余、可解释性差等问题,提出了一种基于SHAP可解释性的焊缝缺陷超声识别XGBoost(极限梯度提升)模型。在碳钢焊缝试样上加工4类典型缺陷,采用横波斜入射法采集超声反射回波信号,分别提取16个时域特征、16个频域特征以及3个信息熵特征。计算SHAP值并选择其前8个高贡献特征构建特征子集,利用交叉验证和网格搜索优化XGBoost模型进行缺陷识别。试验结果表明,4种缺陷识别的平均准确率为96.7%;其中,横通孔的识别效果最佳,精确率、召回率和F_(1-score)均达到100%,三角槽次之,方形槽略差,矩形槽的识别结果最差,其精确率、召回率和F_(1-score)均为93.3%。最后,讨论了高贡献特征与缺陷类别之间的相关性,并对特征贡献差异及其原因进行了分析。Aiming at the problems of feature redundancy and poor interpretability in the process of ultrasonic recognition of weld defects based on machine learning,an XGBoost model for ultrasonic recognition of weld defects was proposed based on SHAP interpretability.Four kinds of typical defects were machined on carbon steel weld samples.The ultrasonic reflection echo signal was collected by shear wave oblique incidence method,and 16 time-domain features,16 frequency-domain features and 3 information entropy features were extracted respectively.SHAP values were calculated and 8 high-contribution features were selected to build a feature subset.Cross-validation and grid search were utilized to optimize the XGBoost model.The feature subset was used as input to identify the defect types.The results showed that average recognition accuracy of the four defects was 96.7%.Among them,the recognition effect of the transverse hole was the best,and the precision,recall and F_(1-score)all reached 100%,followed by the triangular groove,and the square groove was slightly worse.The recognition result of the rectangular groove was relatively poor,and its precision,recall and F_(1-score)were all 93.3%.Finally,the correlation between high contribution features and defect categories was discussed,and the difference of feature contribution and its causes were analyzed.

关 键 词：超声检测 缺陷分类 XGBoost模型 特征选择 SHAP 

分 类 号：TG115.28[金属学及工艺—物理冶金] TB553[金属学及工艺—金属学]