摘要
针对TC6锻件超声检测,微小缺陷回波通常容易淹没在结构噪声和非声学噪声的背散射信号中,造成漏检的问题,提出基于集成学习的微小缺陷存在性判定方法。以含Φ0.6,Φ0.8,Φ1 mm盲孔试验件为研究对象,提取超声检测背散射信号的18个时频域特征参数构成检测数据集;利用核主成分分析(kernel principal component analysis,KPCA)对数据集进行降维,选择累计贡献率达到91.1%的前3个主成分作为信号分类特征向量;分别使用BP神经网络、支持向量机(support vector machine,SVM)、决策树、概率神经网络(probabilistic neural network,PNN)建立单分类模型,并采用集成学习方法,通过加权投票机制对4种分类模型进行融合。测试样本试验结果表明,与单分类模型相比,融合后的模型分类准确率为95.8%,能够有效识别TC6锻件中是否存在微小缺陷,准确率优于单分类模型。
For TC6 forgings ultrasonic testing,the echo of micro defects is usually submerged in the backscattered signal of structural noise and non acoustic noise,which is easy to cause the problem of missing inspection.A method for judging the existence of micro defects based on ensemble learning is proposed.Taking the test pieces with blind holes ofΦ0.6,Φ0.8 andΦ1 mm as the research object,18 time-frequency characteristic parameters of ultrasonic back scatter signal are extracted to form the detection data set,and the kernel principal component analysis(KPCA)is used to analyze the ultrasonic back scatter signal The first three principal components with cumulative contribution rate of 91.1%were selected as the feature vectors of signal classification,and BP neural network and support vector machine(SVM),decision tree and probabilistic neural network (PNN) are used to build a single classification model, and the four classification models are fused byweighted voting mechanism using ensemble learning method. The test results of test samples show that,compared with the single classification model, the classification accuracy of the fusion model is 95.8%, whichcan effectively identify whether there are small defects in TC6 forgings, and the accuracy rate is better than thatof the single classification model.
作者
杨德宸
吴伟
邬冠华
陈曦
YANG Dechen;WU Wei;WU Guanhua;CHEN Xi(Key Laboratory of Nondestructive Testing Technology(Ministry of Education),Nanchang Hangkong University,Nanchang 330063,China)
出处
《中国测试》
CAS
北大核心
2021年第10期95-102,共8页
China Measurement & Test
基金
无损检测教育部重点实验室(南昌航空大学)开放基金(EW201708505)
南昌航空大学研究生创新专项基金(YC2019040)。
关键词
TC6锻件
背散射信号
微小缺陷回波
核主成分分析
集成学习
TC6 forging
back scatter signal
micro defect echo
kernel principal component analysis
ensemble learning
