10 May 2011 Fixed eigenvector analysis of thermographic NDE data
Author Affiliations +
Proceedings Volume 8013, Thermosense: Thermal Infrared Applications XXXIII; 80130T (2011); doi: 10.1117/12.882359
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States
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

Thermal modeling

Principal component analysis

Data modeling

Signal to noise ratio

Nondestructive evaluation



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