Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-fiber-Reinforced Polymers

Version 1 : Received: 30 July 2021 / Approved: 3 August 2021 / Online: 3 August 2021 (15:26:47 CEST)

A peer-reviewed article of this Preprint also exists.

Ebrahimi, S.; Fleuret, J.R.; Klein, M.; Théroux, L.-D.; Ibarra-Castanedo, C.; Maldague, X.P.V. Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers. Sensors 2021, 21, 7185. Ebrahimi, S.; Fleuret, J.R.; Klein, M.; Théroux, L.-D.; Ibarra-Castanedo, C.; Maldague, X.P.V. Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers. Sensors 2021, 21, 7185.

Journal reference: Sensors 2021, 21, 7185
DOI: 10.3390/s21217185

Abstract

Pulsed thermography is a commonly used non-destructive testing method, and is increasingly studied for advanced materials such as carbon fiber-reinforced polymer (CFRP) evaluation. Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, we used a type of matrix decomposition using Robust-PCA via Inexact-ALM in our experiment. We investigate this method as a pre-and post-processing method on thermal data acquired by pulsed thermography. We employed state-of-the-art methods, i.e., PCT, PPT, and PLST, as the main process. The results indicate that pre-processing on thermal data can elevate the defect detectability while post-processing, in some cases, can deteriorate the results.

Keywords

Robust PCA, RPCA, PCP, IALM, Noise Reduction, Pulsed Thermography, CFRP

Subject

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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