Defect depth determination in laser infrared thermography based on LSTM-RNN

Carbon fiber reinforced polymer (CFRP) has been increasingly used in aviation industry since it significantly enhances the performance of aircraft. However, imperfections inside the CFRP structures pose a threat to aviation safety. Apart from the defect shape and size, flaw depth is crucial to asses...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE access 2020-01, Vol.8, p.1-1
Hauptverfasser: Wang, Qiang, Liu, Qiuhan, Xia, Ruicong, Li, Guangyuan, Gao, Jianguo, Zhou, Hongbin, Zhao, Boyan
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1
container_issue
container_start_page 1
container_title IEEE access
container_volume 8
creator Wang, Qiang
Liu, Qiuhan
Xia, Ruicong
Li, Guangyuan
Gao, Jianguo
Zhou, Hongbin
Zhao, Boyan
description Carbon fiber reinforced polymer (CFRP) has been increasingly used in aviation industry since it significantly enhances the performance of aircraft. However, imperfections inside the CFRP structures pose a threat to aviation safety. Apart from the defect shape and size, flaw depth is crucial to assess the defect severity. In this work, we utilize a laser infrared thermography (LIT) system to inspect an aviation CFRP sheet and adopt a long-short term memory recurrent neural network (LSTM-RNN) to determine the defect depth. Thermographic sequences obtained by LIT are processed using thermographic signal reconstructions (TSR) method. Raw data and TSR processed data are separately used to train and test the LSTM-RNN. Results show that background noises in the original thermal signals can be effectively reduced by the TSR method, which is helpful for the models to learn the signal characteristics. Compared with two traditional methods, recurrent neural network (RNN) and convolutional neural network (CNN), we find the LSTM-RNN outperforms.
doi_str_mv 10.1109/ACCESS.2020.3018116
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_ACCESS_2020_3018116</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9172057</ieee_id><doaj_id>oai_doaj_org_article_dedfd2fda7054bfd958c91b91a839532</doaj_id><sourcerecordid>2454645435</sourcerecordid><originalsourceid>FETCH-LOGICAL-c408t-e5ac010546d0c511fbc30fbec6d055a0095d718c765b47cea8b9dfc80aeed93</originalsourceid><addsrcrecordid>eNpNUV1PwjAUbYwmEuQX8LLE52G7rtv6SCYqETFxvDddewsjsM6uPPjvLY4Qm7S9X-fcm3sQmhI8IwTzp3lZLqpqluAEzygmBSHZDRolJOMxZTS7_Wffo0nf73E4RQixfITen8GA8pGGzu_C68Edm1b6xrZR00YH2YMLhnHSgY78LqTt1slu9xPVIaejULeqNh_x13r9gO6MPPQwufxjVL0sNuVbvPp8XZbzVaxSXPgYmFSYYJZmGitGiKkVxaYGFXzGJMac6ZwUKs9YneYKZFFzbVSBJYDmdIyWA6u2ci861xyl-xFWNuIvYN1WSOcbdQChQRudGC3z0K02mrNCcVJzIgvKGU0C1-PA1Tn7fYLei709uTYML5I0DBhuWNsY0aFKOdv3Dsy1K8HiLIEYJBBnCcRFgoCaDqgGAK4ITvIEs5z-Aqklggo</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2454645435</pqid></control><display><type>article</type><title>Defect depth determination in laser infrared thermography based on LSTM-RNN</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Wang, Qiang ; Liu, Qiuhan ; Xia, Ruicong ; Li, Guangyuan ; Gao, Jianguo ; Zhou, Hongbin ; Zhao, Boyan</creator><creatorcontrib>Wang, Qiang ; Liu, Qiuhan ; Xia, Ruicong ; Li, Guangyuan ; Gao, Jianguo ; Zhou, Hongbin ; Zhao, Boyan</creatorcontrib><description>Carbon fiber reinforced polymer (CFRP) has been increasingly used in aviation industry since it significantly enhances the performance of aircraft. However, imperfections inside the CFRP structures pose a threat to aviation safety. Apart from the defect shape and size, flaw depth is crucial to assess the defect severity. In this work, we utilize a laser infrared thermography (LIT) system to inspect an aviation CFRP sheet and adopt a long-short term memory recurrent neural network (LSTM-RNN) to determine the defect depth. Thermographic sequences obtained by LIT are processed using thermographic signal reconstructions (TSR) method. Raw data and TSR processed data are separately used to train and test the LSTM-RNN. Results show that background noises in the original thermal signals can be effectively reduced by the TSR method, which is helpful for the models to learn the signal characteristics. Compared with two traditional methods, recurrent neural network (RNN) and convolutional neural network (CNN), we find the LSTM-RNN outperforms.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.3018116</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Air safety ; Aircraft performance ; Aircraft safety ; Artificial neural networks ; Aviation ; Background noise ; Carbon fiber reinforced plastics ; CFRP ; Depth determination ; Fiber reinforced polymers ; Infrared imaging ; Infrared lasers ; Laser infrared thermography (LIT) ; Neural network (NN) ; Neural networks ; Recurrent neural networks ; Short term ; Signal processing ; Thermographic signal reconstruction (TSR) ; Thermography</subject><ispartof>IEEE access, 2020-01, Vol.8, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-e5ac010546d0c511fbc30fbec6d055a0095d718c765b47cea8b9dfc80aeed93</citedby><cites>FETCH-LOGICAL-c408t-e5ac010546d0c511fbc30fbec6d055a0095d718c765b47cea8b9dfc80aeed93</cites><orcidid>0000-0003-2225-5138 ; 0000-0003-3736-8717 ; 0000-0001-5679-6077 ; 0000-0002-9478-4357 ; 0000-0002-2824-2277</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9172057$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,27633,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Wang, Qiang</creatorcontrib><creatorcontrib>Liu, Qiuhan</creatorcontrib><creatorcontrib>Xia, Ruicong</creatorcontrib><creatorcontrib>Li, Guangyuan</creatorcontrib><creatorcontrib>Gao, Jianguo</creatorcontrib><creatorcontrib>Zhou, Hongbin</creatorcontrib><creatorcontrib>Zhao, Boyan</creatorcontrib><title>Defect depth determination in laser infrared thermography based on LSTM-RNN</title><title>IEEE access</title><addtitle>Access</addtitle><description>Carbon fiber reinforced polymer (CFRP) has been increasingly used in aviation industry since it significantly enhances the performance of aircraft. However, imperfections inside the CFRP structures pose a threat to aviation safety. Apart from the defect shape and size, flaw depth is crucial to assess the defect severity. In this work, we utilize a laser infrared thermography (LIT) system to inspect an aviation CFRP sheet and adopt a long-short term memory recurrent neural network (LSTM-RNN) to determine the defect depth. Thermographic sequences obtained by LIT are processed using thermographic signal reconstructions (TSR) method. Raw data and TSR processed data are separately used to train and test the LSTM-RNN. Results show that background noises in the original thermal signals can be effectively reduced by the TSR method, which is helpful for the models to learn the signal characteristics. Compared with two traditional methods, recurrent neural network (RNN) and convolutional neural network (CNN), we find the LSTM-RNN outperforms.</description><subject>Air safety</subject><subject>Aircraft performance</subject><subject>Aircraft safety</subject><subject>Artificial neural networks</subject><subject>Aviation</subject><subject>Background noise</subject><subject>Carbon fiber reinforced plastics</subject><subject>CFRP</subject><subject>Depth determination</subject><subject>Fiber reinforced polymers</subject><subject>Infrared imaging</subject><subject>Infrared lasers</subject><subject>Laser infrared thermography (LIT)</subject><subject>Neural network (NN)</subject><subject>Neural networks</subject><subject>Recurrent neural networks</subject><subject>Short term</subject><subject>Signal processing</subject><subject>Thermographic signal reconstruction (TSR)</subject><subject>Thermography</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUV1PwjAUbYwmEuQX8LLE52G7rtv6SCYqETFxvDddewsjsM6uPPjvLY4Qm7S9X-fcm3sQmhI8IwTzp3lZLqpqluAEzygmBSHZDRolJOMxZTS7_Wffo0nf73E4RQixfITen8GA8pGGzu_C68Edm1b6xrZR00YH2YMLhnHSgY78LqTt1slu9xPVIaejULeqNh_x13r9gO6MPPQwufxjVL0sNuVbvPp8XZbzVaxSXPgYmFSYYJZmGitGiKkVxaYGFXzGJMac6ZwUKs9YneYKZFFzbVSBJYDmdIyWA6u2ci861xyl-xFWNuIvYN1WSOcbdQChQRudGC3z0K02mrNCcVJzIgvKGU0C1-PA1Tn7fYLei709uTYML5I0DBhuWNsY0aFKOdv3Dsy1K8HiLIEYJBBnCcRFgoCaDqgGAK4ITvIEs5z-Aqklggo</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Wang, Qiang</creator><creator>Liu, Qiuhan</creator><creator>Xia, Ruicong</creator><creator>Li, Guangyuan</creator><creator>Gao, Jianguo</creator><creator>Zhou, Hongbin</creator><creator>Zhao, Boyan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-2225-5138</orcidid><orcidid>https://orcid.org/0000-0003-3736-8717</orcidid><orcidid>https://orcid.org/0000-0001-5679-6077</orcidid><orcidid>https://orcid.org/0000-0002-9478-4357</orcidid><orcidid>https://orcid.org/0000-0002-2824-2277</orcidid></search><sort><creationdate>20200101</creationdate><title>Defect depth determination in laser infrared thermography based on LSTM-RNN</title><author>Wang, Qiang ; Liu, Qiuhan ; Xia, Ruicong ; Li, Guangyuan ; Gao, Jianguo ; Zhou, Hongbin ; Zhao, Boyan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-e5ac010546d0c511fbc30fbec6d055a0095d718c765b47cea8b9dfc80aeed93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Air safety</topic><topic>Aircraft performance</topic><topic>Aircraft safety</topic><topic>Artificial neural networks</topic><topic>Aviation</topic><topic>Background noise</topic><topic>Carbon fiber reinforced plastics</topic><topic>CFRP</topic><topic>Depth determination</topic><topic>Fiber reinforced polymers</topic><topic>Infrared imaging</topic><topic>Infrared lasers</topic><topic>Laser infrared thermography (LIT)</topic><topic>Neural network (NN)</topic><topic>Neural networks</topic><topic>Recurrent neural networks</topic><topic>Short term</topic><topic>Signal processing</topic><topic>Thermographic signal reconstruction (TSR)</topic><topic>Thermography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Qiang</creatorcontrib><creatorcontrib>Liu, Qiuhan</creatorcontrib><creatorcontrib>Xia, Ruicong</creatorcontrib><creatorcontrib>Li, Guangyuan</creatorcontrib><creatorcontrib>Gao, Jianguo</creatorcontrib><creatorcontrib>Zhou, Hongbin</creatorcontrib><creatorcontrib>Zhao, Boyan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Qiang</au><au>Liu, Qiuhan</au><au>Xia, Ruicong</au><au>Li, Guangyuan</au><au>Gao, Jianguo</au><au>Zhou, Hongbin</au><au>Zhao, Boyan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Defect depth determination in laser infrared thermography based on LSTM-RNN</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2020-01-01</date><risdate>2020</risdate><volume>8</volume><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>Carbon fiber reinforced polymer (CFRP) has been increasingly used in aviation industry since it significantly enhances the performance of aircraft. However, imperfections inside the CFRP structures pose a threat to aviation safety. Apart from the defect shape and size, flaw depth is crucial to assess the defect severity. In this work, we utilize a laser infrared thermography (LIT) system to inspect an aviation CFRP sheet and adopt a long-short term memory recurrent neural network (LSTM-RNN) to determine the defect depth. Thermographic sequences obtained by LIT are processed using thermographic signal reconstructions (TSR) method. Raw data and TSR processed data are separately used to train and test the LSTM-RNN. Results show that background noises in the original thermal signals can be effectively reduced by the TSR method, which is helpful for the models to learn the signal characteristics. Compared with two traditional methods, recurrent neural network (RNN) and convolutional neural network (CNN), we find the LSTM-RNN outperforms.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.3018116</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-2225-5138</orcidid><orcidid>https://orcid.org/0000-0003-3736-8717</orcidid><orcidid>https://orcid.org/0000-0001-5679-6077</orcidid><orcidid>https://orcid.org/0000-0002-9478-4357</orcidid><orcidid>https://orcid.org/0000-0002-2824-2277</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2169-3536
ispartof IEEE access, 2020-01, Vol.8, p.1-1
issn 2169-3536
2169-3536
language eng
recordid cdi_crossref_primary_10_1109_ACCESS_2020_3018116
source IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Air safety
Aircraft performance
Aircraft safety
Artificial neural networks
Aviation
Background noise
Carbon fiber reinforced plastics
CFRP
Depth determination
Fiber reinforced polymers
Infrared imaging
Infrared lasers
Laser infrared thermography (LIT)
Neural network (NN)
Neural networks
Recurrent neural networks
Short term
Signal processing
Thermographic signal reconstruction (TSR)
Thermography
title Defect depth determination in laser infrared thermography based on LSTM-RNN
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T18%3A21%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Defect%20depth%20determination%20in%20laser%20infrared%20thermography%20based%20on%20LSTM-RNN&rft.jtitle=IEEE%20access&rft.au=Wang,%20Qiang&rft.date=2020-01-01&rft.volume=8&rft.spage=1&rft.epage=1&rft.pages=1-1&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2020.3018116&rft_dat=%3Cproquest_cross%3E2454645435%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2454645435&rft_id=info:pmid/&rft_ieee_id=9172057&rft_doaj_id=oai_doaj_org_article_dedfd2fda7054bfd958c91b91a839532&rfr_iscdi=true