基于电磁超声的小样本铝板表面缺陷检测方法  

A small sample aluminum plate surface defect detection method based on electromagnetic ultrasound

作　　者：杨斌 易朋兴[1] 郝峥旭 Yang Bin;Yi Pengxing;Hao Zhengxu(School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学机械科学与工程学院,武汉430074

出　　处：《电子测量技术》2024年第3期109-115,共7页Electronic Measurement Technology

基　　金：国家重点研发计划(2018YFB2003303)项目资助。

摘　　要：使用基于电磁超声技术的智能缺陷检测算法可以实现对重要零件质量状态的监测,保证设备安全可靠运行。在实际检测过程中,一方面采集的信号往往会被噪声污染进而对检测结果造成干扰,另一方面重要零件缺陷信号往往数据量较少不能满足神经网络训练的需求。因此本文提出了一种基于变分模态分解的降噪算法对检测信号进行降噪预处理以提升信号质量,提出了一种改进型的虚拟样本生成技术用来扩充样本集,并使用迁移学习技术减少神经网络训练的参数量以解决样本数量不足的问题。在铝板表面缺陷的深度检测样例中该方法达到了97.2%的平均预测准确率,因此该方法对非铁磁性材料表面缺陷检测有一定的借鉴意义。The intelligent defect detection algorithm based on electromagnetic ultrasonic technology can be used to monitor the quality status of important parts and ensure the safe and reliable operation of equipment.In the actual detection process,on the one hand,the collected signals are often polluted by noise,which interferes with the detection results.On the other hand,the defect signals of important parts often have less data and cannot meet the needs of neural network training.Therefore,this paper proposes a noise reduction algorithm based on variational mode decomposition to pre-process detected signals to improve signal quality,proposes an improved virtual sample generation technology to expand the sample set,and uses transfer learning technology to reduce the number of parameters in neural network training to solve the problem of insufficient sample number.The average prediction accuracy of this method is 97.2%in the depth detection example of aluminum plate surface defects.Therefore,this method has certain reference significance for the surface defect detection of non-ferromagnetic materials.

关 键 词：小样本 迁移学习 变分模态分解 缺陷检测 

分 类 号：TB529[理学—物理]