Application of principal component analysis in dynamic thermal testing data processing
Source abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Thermography and Photoacoustic Techniques · Calibration and Measurement Techniques · Ultrasonics and Acoustic Wave Propagation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (7)
Show source-provided references
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