文章基本信息

标题：Infrared Non-Destructive Testing via Semi-Nonnegative Matrix Factorization
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作者：Bardia Yousefi ; Clemente Ibarra-Castanedo ; Xavier P.V. Maldague 等
期刊名称：Proceedings
电子版ISSN：2504-3900
出版年度：2019
卷号：27
期号：1
页码：13
DOI：10.3390/proceedings2019027013
语种：English
出版社：MDPI AG
摘要：Detection of subsurface defects is undeniably a growing subfield of infrared non-destructive testing (IR-NDT). There are many algorithms used for this purpose, where non-negative matrix factorization (NMF) is considered to be an interesting alternative to principal component analysis (PCA) by having no negative basis in matrix decomposition. Here, an application of Semi non-negative matrix factorization (Semi-NMF) in IR-NDT is presented to determine the subsurface defects of an Aluminum plate specimen through active thermographic method. To benchmark, the defect detection accuracy and computational load of the Semi-NMF approach is compared to state-of-the-art thermography processing approaches such as: principal component thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT), Sparse PCT, Sparse NMF and standard NMF with gradient descend (GD) and non-negative least square (NNLS). The results show 86% accuracy for 27.5s computational time for SemiNMF, which conclusively indicate the promising performance of the approach in the field of IR-NDT.