10 May 2011 Fixed eigenvector analysis of thermographic NDE data
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Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. This paper will discuss an alternative method of analysis that has been developed where a predetermined set of eigenvectors is used to process the thermal data from both reinforced carbon-carbon (RCC) and graphite-epoxy honeycomb materials. These eigenvectors can be generated either from an analytic model of the thermal response of the material system under examination, or from a large set of experimental data. This paper provides the details of the analytic model, an overview of the PCA process, as well as a quantitative signal-to-noise comparison of the results of performing both conventional PCA and fixed eigenvector analysis on thermographic data from two specimens, one Reinforced Carbon-Carbon with flat bottom holes and the second a sandwich construction with graphite-epoxy face sheets and aluminum honeycomb core.
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K. Elliott Cramer, William P. Winfree, "Fixed eigenvector analysis of thermographic NDE data", Proc. SPIE 8013, Thermosense: Thermal Infrared Applications XXXIII, 80130T (10 May 2011); doi: 10.1117/12.882359; https://doi.org/10.1117/12.882359

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