Application of principal component analysis in dynamic thermal testing data processing

Research output: Contribution to journalArticle

9 Citations (Scopus)

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 languageEnglish
Pages (from-to)509-516
Number of pages8
JournalRussian Journal of Nondestructive Testing
Volume44
Issue number7
DOIs
Publication statusPublished - Jul 2008

Fingerprint

principal components analysis
Principal component analysis
Fourier analysis
Testing
Aluminum
moisture content
Phase diagrams
Signal to noise ratio
corrosion
signal to noise ratios
Moisture
phase diagrams
Corrosion
aluminum
Defects
composite materials
defects
Composite materials
Hot Temperature

ASJC Scopus subject areas

  • Mechanical Engineering
  • Mechanics of Materials
  • Condensed Matter Physics
  • Materials Science(all)

Cite this

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