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...
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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. |
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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. 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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. 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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 |
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