Probability-Based Diagnostic Imaging of Fatigue Damage in Carbon Fiber Composites Using Sparse Representation of Lamb Waves
Carbon fiber composites are commonly used in aerospace and other fields due to their excellent properties, and fatigue damage will occur in the process of service. Damage imaging can be performed using damage probability imaging methods to obtain the fatigue damage condition of carbon fiber composit...
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Veröffentlicht in: | Electronics (Basel) 2023-03, Vol.12 (5), p.1148 |
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description | Carbon fiber composites are commonly used in aerospace and other fields due to their excellent properties, and fatigue damage will occur in the process of service. Damage imaging can be performed using damage probability imaging methods to obtain the fatigue damage condition of carbon fiber composites. At present, the damage factor commonly used in the damage probability imaging algorithm has low contrast and poor anti-noise performance, which leads to artifacts in the imaging and misjudgment of the damaged area. Therefore, this paper proposes a fatigue damage probability imaging method for carbon fiber composite materials based on the sparse representation of Lamb wave signals. Based on constructing the Lamb wave dictionary, a fast block sparse Bayesian learning algorithm is used to represent the Lamb wave signals sparsely, and the definition of Lamb wave sparse representing the damage factor calculates the damage probability of the monitoring area and then images the fatigue damage of the carbon fiber composite materials. The imaging research was carried out using the fatigue monitoring experiment data of NASA’s carbon fiber composite materials. The results show that the proposed damage factor can clearly distinguish the damaged area from the undamaged area and has strong noise immunity. Compared with the energy damage factor and the cross-correlation damage factor, the error percentages are reduced by at least 58.63%, 28.11%, and 8.43% for signal-to-noise ratios of 6 dB, 3 dB, and 0.1 dB, respectively, after adding noise to the signal. The results can more accurately reflect the real location and area of fatigue damage in carbon fiber composites. |
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Damage imaging can be performed using damage probability imaging methods to obtain the fatigue damage condition of carbon fiber composites. At present, the damage factor commonly used in the damage probability imaging algorithm has low contrast and poor anti-noise performance, which leads to artifacts in the imaging and misjudgment of the damaged area. Therefore, this paper proposes a fatigue damage probability imaging method for carbon fiber composite materials based on the sparse representation of Lamb wave signals. Based on constructing the Lamb wave dictionary, a fast block sparse Bayesian learning algorithm is used to represent the Lamb wave signals sparsely, and the definition of Lamb wave sparse representing the damage factor calculates the damage probability of the monitoring area and then images the fatigue damage of the carbon fiber composite materials. The imaging research was carried out using the fatigue monitoring experiment data of NASA’s carbon fiber composite materials. The results show that the proposed damage factor can clearly distinguish the damaged area from the undamaged area and has strong noise immunity. Compared with the energy damage factor and the cross-correlation damage factor, the error percentages are reduced by at least 58.63%, 28.11%, and 8.43% for signal-to-noise ratios of 6 dB, 3 dB, and 0.1 dB, respectively, after adding noise to the signal. The results can more accurately reflect the real location and area of fatigue damage in carbon fiber composites.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12051148</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Carbon fibers ; Composite materials ; Convex analysis ; Cross correlation ; Damage ; Data mining ; Diagnostic imaging ; Dictionaries ; Error reduction ; Fatigue ; Fatigue failure ; Fatigue testing machines ; Fiber composites ; Imaging ; Imaging systems ; Lamb waves ; Machine learning ; Materials ; Mechanical properties ; Methods ; Monitoring ; Noise levels ; Optimization algorithms ; Representations ; Signal processing ; Structural analysis (Engineering) ; Testing ; Wavelet transforms</subject><ispartof>Electronics (Basel), 2023-03, Vol.12 (5), p.1148</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-f6c44301eb1c509d26985f247917bce41f2012d0d56aef1eb25fa2f243b3b5cb3</citedby><cites>FETCH-LOGICAL-c389t-f6c44301eb1c509d26985f247917bce41f2012d0d56aef1eb25fa2f243b3b5cb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>Duan, Qiming</creatorcontrib><creatorcontrib>Ye, Bo</creatorcontrib><creatorcontrib>Zou, Yangkun</creatorcontrib><creatorcontrib>Hua, Rong</creatorcontrib><creatorcontrib>Feng, Jiqi</creatorcontrib><creatorcontrib>Shi, Xiaoxiao</creatorcontrib><title>Probability-Based Diagnostic Imaging of Fatigue Damage in Carbon Fiber Composites Using Sparse Representation of Lamb Waves</title><title>Electronics (Basel)</title><description>Carbon fiber composites are commonly used in aerospace and other fields due to their excellent properties, and fatigue damage will occur in the process of service. Damage imaging can be performed using damage probability imaging methods to obtain the fatigue damage condition of carbon fiber composites. At present, the damage factor commonly used in the damage probability imaging algorithm has low contrast and poor anti-noise performance, which leads to artifacts in the imaging and misjudgment of the damaged area. Therefore, this paper proposes a fatigue damage probability imaging method for carbon fiber composite materials based on the sparse representation of Lamb wave signals. Based on constructing the Lamb wave dictionary, a fast block sparse Bayesian learning algorithm is used to represent the Lamb wave signals sparsely, and the definition of Lamb wave sparse representing the damage factor calculates the damage probability of the monitoring area and then images the fatigue damage of the carbon fiber composite materials. The imaging research was carried out using the fatigue monitoring experiment data of NASA’s carbon fiber composite materials. The results show that the proposed damage factor can clearly distinguish the damaged area from the undamaged area and has strong noise immunity. Compared with the energy damage factor and the cross-correlation damage factor, the error percentages are reduced by at least 58.63%, 28.11%, and 8.43% for signal-to-noise ratios of 6 dB, 3 dB, and 0.1 dB, respectively, after adding noise to the signal. The results can more accurately reflect the real location and area of fatigue damage in carbon fiber composites.</description><subject>Algorithms</subject><subject>Carbon fibers</subject><subject>Composite materials</subject><subject>Convex analysis</subject><subject>Cross correlation</subject><subject>Damage</subject><subject>Data mining</subject><subject>Diagnostic imaging</subject><subject>Dictionaries</subject><subject>Error reduction</subject><subject>Fatigue</subject><subject>Fatigue failure</subject><subject>Fatigue testing machines</subject><subject>Fiber composites</subject><subject>Imaging</subject><subject>Imaging systems</subject><subject>Lamb waves</subject><subject>Machine learning</subject><subject>Materials</subject><subject>Mechanical properties</subject><subject>Methods</subject><subject>Monitoring</subject><subject>Noise levels</subject><subject>Optimization algorithms</subject><subject>Representations</subject><subject>Signal processing</subject><subject>Structural analysis (Engineering)</subject><subject>Testing</subject><subject>Wavelet transforms</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptkU1LAzEQhhdRsFR_gZeA56352O1ujrW1WigofuBxSbKTJaWbrEkqFP-8EQUVOnOY4eV53zlMll0QPGGM4yvYgoreWaMCobgkpKiPshHFFc855fT4z36anYewwak4YTXDo-zjwTsppNmauM-vRYAWLYzorAvRKLTqRWdsh5xGSxFNtwO0EEkDZCyaCy-dRUsjwaO56wcXTISAXsKX5WkQPgB6hMFDABuTPcEpaC16iV7FO4Sz7ESLbYDznznOXpY3z_O7fH1_u5rP1rliNY-5nqqiYJiAJKrEvKVTXpeaFhUnlVRQEE0xoS1uy6kAnTBaakETwCSTpZJsnF1-5w7eve0gxGbjdt6mkw2t6pLUFeP1L9WJLTTGahe9UL0JqplVBakKTBhO1OQAlbqF3ihnQZuk_zOwb4PyLgQPuhm86YXfNwQ3X_9rDvyPfQLwhZBd</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Duan, Qiming</creator><creator>Ye, Bo</creator><creator>Zou, Yangkun</creator><creator>Hua, Rong</creator><creator>Feng, Jiqi</creator><creator>Shi, Xiaoxiao</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20230301</creationdate><title>Probability-Based Diagnostic Imaging of Fatigue Damage in Carbon Fiber Composites Using Sparse Representation of Lamb Waves</title><author>Duan, Qiming ; Ye, Bo ; Zou, Yangkun ; Hua, Rong ; Feng, Jiqi ; Shi, Xiaoxiao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-f6c44301eb1c509d26985f247917bce41f2012d0d56aef1eb25fa2f243b3b5cb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Carbon fibers</topic><topic>Composite materials</topic><topic>Convex analysis</topic><topic>Cross correlation</topic><topic>Damage</topic><topic>Data mining</topic><topic>Diagnostic imaging</topic><topic>Dictionaries</topic><topic>Error reduction</topic><topic>Fatigue</topic><topic>Fatigue failure</topic><topic>Fatigue testing machines</topic><topic>Fiber composites</topic><topic>Imaging</topic><topic>Imaging systems</topic><topic>Lamb waves</topic><topic>Machine learning</topic><topic>Materials</topic><topic>Mechanical properties</topic><topic>Methods</topic><topic>Monitoring</topic><topic>Noise levels</topic><topic>Optimization algorithms</topic><topic>Representations</topic><topic>Signal processing</topic><topic>Structural analysis (Engineering)</topic><topic>Testing</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Duan, Qiming</creatorcontrib><creatorcontrib>Ye, Bo</creatorcontrib><creatorcontrib>Zou, Yangkun</creatorcontrib><creatorcontrib>Hua, Rong</creatorcontrib><creatorcontrib>Feng, Jiqi</creatorcontrib><creatorcontrib>Shi, Xiaoxiao</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Duan, Qiming</au><au>Ye, Bo</au><au>Zou, Yangkun</au><au>Hua, Rong</au><au>Feng, Jiqi</au><au>Shi, Xiaoxiao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Probability-Based Diagnostic Imaging of Fatigue Damage in Carbon Fiber Composites Using Sparse Representation of Lamb Waves</atitle><jtitle>Electronics (Basel)</jtitle><date>2023-03-01</date><risdate>2023</risdate><volume>12</volume><issue>5</issue><spage>1148</spage><pages>1148-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>Carbon fiber composites are commonly used in aerospace and other fields due to their excellent properties, and fatigue damage will occur in the process of service. Damage imaging can be performed using damage probability imaging methods to obtain the fatigue damage condition of carbon fiber composites. At present, the damage factor commonly used in the damage probability imaging algorithm has low contrast and poor anti-noise performance, which leads to artifacts in the imaging and misjudgment of the damaged area. Therefore, this paper proposes a fatigue damage probability imaging method for carbon fiber composite materials based on the sparse representation of Lamb wave signals. Based on constructing the Lamb wave dictionary, a fast block sparse Bayesian learning algorithm is used to represent the Lamb wave signals sparsely, and the definition of Lamb wave sparse representing the damage factor calculates the damage probability of the monitoring area and then images the fatigue damage of the carbon fiber composite materials. The imaging research was carried out using the fatigue monitoring experiment data of NASA’s carbon fiber composite materials. The results show that the proposed damage factor can clearly distinguish the damaged area from the undamaged area and has strong noise immunity. Compared with the energy damage factor and the cross-correlation damage factor, the error percentages are reduced by at least 58.63%, 28.11%, and 8.43% for signal-to-noise ratios of 6 dB, 3 dB, and 0.1 dB, respectively, after adding noise to the signal. The results can more accurately reflect the real location and area of fatigue damage in carbon fiber composites.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics12051148</doi><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Carbon fibers Composite materials Convex analysis Cross correlation Damage Data mining Diagnostic imaging Dictionaries Error reduction Fatigue Fatigue failure Fatigue testing machines Fiber composites Imaging Imaging systems Lamb waves Machine learning Materials Mechanical properties Methods Monitoring Noise levels Optimization algorithms Representations Signal processing Structural analysis (Engineering) Testing Wavelet transforms |
title | Probability-Based Diagnostic Imaging of Fatigue Damage in Carbon Fiber Composites Using Sparse Representation of Lamb Waves |
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