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Proceedings Paper

Principal component analysis of thermographic data
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Paper Abstract

Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. While a reliable technique for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the "good" material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued where a fixed set of eigenvectors, generated from an analytic model of the thermal response of the material under examination, is used to process the thermal data from composite materials. This method has been applied for characterization of flaws.

Paper Details

Date Published: 12 May 2015
PDF: 11 pages
Proc. SPIE 9485, Thermosense: Thermal Infrared Applications XXXVII, 94850S (12 May 2015); doi: 10.1117/12.2176285
Show Author Affiliations
William P. Winfree, NASA Langley Research Ctr. (United States)
K. Elliott Cramer, NASA Langley Research Ctr. (United States)
Joseph N. Zalameda, NASA Langley Research Ctr. (United States)
Patricia A. Howell, NASA Langley Research Ctr. (United States)
Eric R. Burke, NASA Langley Research Ctr. (United States)


Published in SPIE Proceedings Vol. 9485:
Thermosense: Thermal Infrared Applications XXXVII
Sheng-Jen (Tony) Hsieh; Joseph N. Zalameda, Editor(s)

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