Abstract
The principles and features of the application of statistical principal component analysis (PCA) in active thermal testing are considered. A comparison between PCA and Fourier analysis in finding defects in composite materials, detecting corrosion in aluminum, and determining moisture content in construction materials is performed. It is concluded that, generally, images of principal components increase the signal-to-noise ratio and are close in performance to phase diagrams; nevertheless, the results of this method are poorly predictable and require further analysis.
| Original language | English |
|---|---|
| Pages (from-to) | 509-516 |
| Number of pages | 8 |
| Journal | Russian Journal of Nondestructive Testing |
| Volume | 44 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - Jul 2008 |
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ASJC Scopus subject areas
- Mechanical Engineering
- Mechanics of Materials
- Condensed Matter Physics
- Materials Science(all)
Cite this
Application of principal component analysis in dynamic thermal testing data processing. / Vavilov, V. P.; Nesteruk, D. A.; Shiryaev, V. V.; Swiderski, W.
In: Russian Journal of Nondestructive Testing, Vol. 44, No. 7, 07.2008, p. 509-516.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Application of principal component analysis in dynamic thermal testing data processing
AU - Vavilov, V. P.
AU - Nesteruk, D. A.
AU - Shiryaev, V. V.
AU - Swiderski, W.
PY - 2008/7
Y1 - 2008/7
N2 - The principles and features of the application of statistical principal component analysis (PCA) in active thermal testing are considered. A comparison between PCA and Fourier analysis in finding defects in composite materials, detecting corrosion in aluminum, and determining moisture content in construction materials is performed. It is concluded that, generally, images of principal components increase the signal-to-noise ratio and are close in performance to phase diagrams; nevertheless, the results of this method are poorly predictable and require further analysis.
AB - The principles and features of the application of statistical principal component analysis (PCA) in active thermal testing are considered. A comparison between PCA and Fourier analysis in finding defects in composite materials, detecting corrosion in aluminum, and determining moisture content in construction materials is performed. It is concluded that, generally, images of principal components increase the signal-to-noise ratio and are close in performance to phase diagrams; nevertheless, the results of this method are poorly predictable and require further analysis.
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UR - http://www.scopus.com/inward/citedby.url?scp=54249166111&partnerID=8YFLogxK
U2 - 10.1134/S1061830908070097
DO - 10.1134/S1061830908070097
M3 - Article
VL - 44
SP - 509
EP - 516
JO - Russian Journal of Nondestructive Testing
JF - Russian Journal of Nondestructive Testing
SN - 1061-8309
IS - 7
ER -