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Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection.

Katherine TuClemente Ibarra CastanedoStefano SfarraYuan YaoXavier P V Maldgue
Published in: Sensors (Basel, Switzerland) (2021)
Infrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition. At each scale, principal component analysis (PCA) is implemented for feature extraction. By visualizing the loading vectors of PCA, the important building structures are highlighted. Compared with principal component thermography that applies PCA directly to raw thermal data, the proposed multiscale analysis method is able to zoom in on different types of structural features.
Keyphrases
  • data analysis
  • machine learning
  • high resolution
  • electronic health record
  • convolutional neural network
  • neural network
  • artificial intelligence
  • single molecule