| Autor: |
Grossutti M; Department of Physics, University of Guelph, Guelph, Ontario N1G 2W1, Canada., D'Amico J; Department of Physics, University of Guelph, Guelph, Ontario N1G 2W1, Canada., Quintal J; Department of Physics, University of Guelph, Guelph, Ontario N1G 2W1, Canada., MacFarlane H; Department of Physics, University of Guelph, Guelph, Ontario N1G 2W1, Canada., Wareham WC; Department of Physics, University of Guelph, Guelph, Ontario N1G 2W1, Canada., Quirk A; Canadian Light Source Incorporated, 44 Innovation Blvd, Saskatoon, Saskatchewan S7N 2V3, Canada., Dutcher JR; Department of Physics, University of Guelph, Guelph, Ontario N1G 2W1, Canada. |
| Abstrakt: |
Hyperspectral infrared (IR) images contain a large amount of highly spatially resolved information about the chemical composition of a sample. However, the analysis of hyperspectral IR imaging data for complex heterogeneous systems can be challenging because of the spectroscopic and spatial complexity of the data. We implement a deep generative modeling approach using a β-variational autoencoder to learn disentangled representations of the generative factors of variance in a data set of cross-linked polyethylene (PEX-a) pipe. We identify three distinct physicochemical factors of aging and degradation learned by the model and apply the trained model to high-resolution hyperspectral IR images of cross-sectional slices of unused virgin, used in-service, and cracked PEX-a pipe. By mapping the learned representations of aging and degradation to the IR images, we extract detailed information on the physicochemical changes that occur during aging, degradation, and cracking in PEX-a pipe. This study shows how representation learning by deep generative modeling can significantly enhance the analysis of high-resolution IR images of complex heterogeneous samples. |
