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

Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in struct...

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Autores principales:	Samira Ebrahimi, Julien R. Fleuret, Matthieu Klein, Louis-Daniel Théroux, Clemente Ibarra-Castanedo, Xavier P. V. Maldague
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Robust PCA RPCA PCP IALM noise reduction pulsed thermography Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/fbf4ae537a4f4dfe9a7d08e6c1a57c35
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spelling	oai:doaj.org-article:fbf4ae537a4f4dfe9a7d08e6c1a57c352021-11-11T19:10:34ZData Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers10.3390/s212171851424-8220https://doaj.org/article/fbf4ae537a4f4dfe9a7d08e6c1a57c352021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7185https://doaj.org/toc/1424-8220Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). 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, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.Samira EbrahimiJulien R. FleuretMatthieu KleinLouis-Daniel ThérouxClemente Ibarra-CastanedoXavier P. V. MaldagueMDPI AGarticleRobust PCARPCAPCPIALMnoise reductionpulsed thermographyChemical technologyTP1-1185ENSensors, Vol 21, Iss 7185, p 7185 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Robust PCA RPCA PCP IALM noise reduction pulsed thermography Chemical technology TP1-1185
spellingShingle	Robust PCA RPCA PCP IALM noise reduction pulsed thermography Chemical technology TP1-1185 Samira Ebrahimi Julien R. Fleuret Matthieu Klein Louis-Daniel Théroux Clemente Ibarra-Castanedo Xavier P. V. Maldague Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers
description	Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). 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, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.
format	article
author	Samira Ebrahimi Julien R. Fleuret Matthieu Klein Louis-Daniel Théroux Clemente Ibarra-Castanedo Xavier P. V. Maldague
author_facet	Samira Ebrahimi Julien R. Fleuret Matthieu Klein Louis-Daniel Théroux Clemente Ibarra-Castanedo Xavier P. V. Maldague
author_sort	Samira Ebrahimi
title	Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers
title_short	Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers
title_full	Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers
title_fullStr	Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers
title_full_unstemmed	Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers
title_sort	data enhancement via low-rank matrix reconstruction in pulsed thermography for carbon-fibre-reinforced polymers
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fbf4ae537a4f4dfe9a7d08e6c1a57c35
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Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers

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