Improvement of defect detection in shearography by using principal component analysis
Improvement of defect detection in shearography by using principal component analysis
复制标题
使用主成分分析改进剪切散斑分析中的缺陷检测
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Georges
中科院分区：
文献类型：
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作者：
J. Vandenrijt;N. Lièvre;M. Georges
提出了一种基于主成分分析（PCA）的剪切图缺陷检测后处理技术。PCA允许将时间序列图像分解为一组称为经验正交函数（EOF）的图像，每个图像显示具有给定时间序列可变性的特征。我们将PCA应用于含不同深度缺陷的复合材料样品，并对其进行了瞬态热波处理。分析时间序列发现，浅层缺陷首先出现，深层缺陷出现较晚。使用PCA，所有的缺陷都出现在一个或两个EOF中，从而简化了缺陷的识别。
A post-processing technique based on principal components analysis (PCA) is proposed for shearography for defect detection. PCA allows decomposing a time series of images into a set of images called Empirical Orthogonal Functions (EOF), each showing features with a given variability in the time series. We have applied PCA on composite samples containing various defects at different depths and which undergo transient thermal wave. Analyzing the temporal series shows the shallow defects appearing first whereas the deeper ones appear later. With PCA all the defects appear in one or two of the EOF, easing the identification of defects.