在巨磁阻脉冲涡流传感器(GMR-PEC)上实现平板导体表面和次表面裂纹缺陷以及孔缺陷进行精确分类。在频率分析基础上, 提出了一种新的缺陷特征量——涡流差分响应信号的功率谱密度。由于主成分分析具有良好的降维特性, 采用主成分分析结合线性判别分类(PCA-LDA)和贝叶斯分类(PCA-Bayes)进行缺陷的分类。结果表明, 基于新的特征量的分类方法能实现导体表面和次表面的裂纹和孔缺陷的精确分类, 在脉冲涡流自动测量领域具有潜在的意义。
所属栏目
2014远东无损检测新技术论坛论文精选国家自然基金资助项目(61178067);山西省青年科学基金资助项目(2013021004-4);太原科技大学博士启动基金资助项目(20132011)。
收稿日期
2014/6/25
作者单位
彭英:太原科技大学 应用科学学院, 太原 030024
吴应发:太原科技大学 应用科学学院, 太原 030024
邱选兵:太原科技大学 应用科学学院, 太原 030024
刘路路:太原科技大学 应用科学学院, 太原 030024
魏计林:太原科技大学 应用科学学院, 太原 030024
陈长飞:山东能源临矿集团古城煤矿, 济宁 272100
备注
彭英(1979-), 女, 博士研究生, 主要从事电磁无损检测、裂纹扩展等研究工作。
引用该论文:
PENG Ying,WU Ying-fa,QIU Xuan-bing,LIU Lu-lu,WEI Ji-lin,CHEN Chang-fei.Defect Classification by Pulsed Eddy Current Technique Based-on Power Spectral Density Analysis[J].Nondestructive Testing,2014,36(12):8～11
彭英,吴应发,邱选兵,刘路路,魏计林,陈长飞.基于功率谱密度分析的脉冲涡流缺陷分类法[J].无损检测,2014,36(12):8～11
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