Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization
Classification results of SVM-PSO In order to identify two failures of crack damage and edge damage to wind turbine blade, a damage identification system was designed by acoustic emission technique. This system took advantage of wireless technique for signal collection and transmission and upper com...
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| Veröffentlicht in: | Journal of robotics and mechatronics 2015-06, Vol.27 (3), p.244-250 |
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| container_title | Journal of robotics and mechatronics |
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| creator | Gu, Guimei Hu, Rang Li, Yuanyuan |
| description | Classification results of SVM-PSO
In order to identify two failures of crack damage and edge damage to wind turbine blade, a damage identification system was designed by acoustic emission technique. This system took advantage of wireless technique for signal collection and transmission and upper computer for receiving and processing data. This system adopted acoustic emission sensor, NRF905 wireless transmission, upper computer designed by VB language, and the serial communication function of VB for data receiving. Data was firstly normalized after being received. Then, the energy features of data were abstracted by db wavelet. With the abstracted features, support vector machine model was established and verified, and the machine parameters were optimized by particle swarm optimization. Results show that the system is reliable in data collection and transmission, and the correctness of damage identification obviously increases by optimizing the support vector machine with particle swarm. The design provides method to monitor the status of rotating object, so this system can provide model base for subsequent studies. |
| doi_str_mv | 10.20965/jrm.2015.p0244 |
| format | Article |
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Classification results of SVM-PSO
In order to identify two failures of crack damage and edge damage to wind turbine blade, a damage identification system was designed by acoustic emission technique. This system took advantage of wireless technique for signal collection and transmission and upper computer for receiving and processing data. This system adopted acoustic emission sensor, NRF905 wireless transmission, upper computer designed by VB language, and the serial communication function of VB for data receiving. Data was firstly normalized after being received. Then, the energy features of data were abstracted by db wavelet. With the abstracted features, support vector machine model was established and verified, and the machine parameters were optimized by particle swarm optimization. Results show that the system is reliable in data collection and transmission, and the correctness of damage identification obviously increases by optimizing the support vector machine with particle swarm. The design provides method to monitor the status of rotating object, so this system can provide model base for subsequent studies.</description><identifier>ISSN: 0915-3942</identifier><identifier>EISSN: 1883-8049</identifier><identifier>DOI: 10.20965/jrm.2015.p0244</identifier><language>eng</language><publisher>Tokyo: Fuji Technology Press Co. Ltd</publisher><subject>Acoustic emission ; Damage detection ; Data collection ; Data processing ; Particle swarm optimization ; Receiving ; Signal processing ; Support vector machines ; Turbine blades ; Wind damage ; Wind turbines</subject><ispartof>Journal of robotics and mechatronics, 2015-06, Vol.27 (3), p.244-250</ispartof><rights>Copyright © 2015 Fuji Technology Press Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c390t-64d98c740fb4276d83562f9bbea0e975d75e37865ac049476e7a91bd13984db23</citedby><cites>FETCH-LOGICAL-c390t-64d98c740fb4276d83562f9bbea0e975d75e37865ac049476e7a91bd13984db23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,27901,27902</link.rule.ids></links><search><creatorcontrib>Gu, Guimei</creatorcontrib><creatorcontrib>Hu, Rang</creatorcontrib><creatorcontrib>Li, Yuanyuan</creatorcontrib><creatorcontrib>School of Automation and Electrical Engineering, Lanzhou Jiaotong University</creatorcontrib><title>Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization</title><title>Journal of robotics and mechatronics</title><description><div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270003/03.jpg"" width=""340"" />
Classification results of SVM-PSO
In order to identify two failures of crack damage and edge damage to wind turbine blade, a damage identification system was designed by acoustic emission technique. This system took advantage of wireless technique for signal collection and transmission and upper computer for receiving and processing data. This system adopted acoustic emission sensor, NRF905 wireless transmission, upper computer designed by VB language, and the serial communication function of VB for data receiving. Data was firstly normalized after being received. Then, the energy features of data were abstracted by db wavelet. With the abstracted features, support vector machine model was established and verified, and the machine parameters were optimized by particle swarm optimization. Results show that the system is reliable in data collection and transmission, and the correctness of damage identification obviously increases by optimizing the support vector machine with particle swarm. The design provides method to monitor the status of rotating object, so this system can provide model base for subsequent studies.</description><subject>Acoustic emission</subject><subject>Damage detection</subject><subject>Data collection</subject><subject>Data processing</subject><subject>Particle swarm optimization</subject><subject>Receiving</subject><subject>Signal processing</subject><subject>Support vector machines</subject><subject>Turbine blades</subject><subject>Wind damage</subject><subject>Wind turbines</subject><issn>0915-3942</issn><issn>1883-8049</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNotkE1LAzEQhoMoWGrPXgOet002ySY5av0qVCq06jFkN1lN6W7WbJZSf71pdQ7zAe-8wzwAXGM0zZEs2GwbmtRhNu1QTukZGGEhSCYQledghCRmGZE0vwSTvt-iFIxySfgI7NdxMAfoW7gwto2udpWOLo2-hve60Z8WRg8_XGvgZgilay2822mTsu6tOe6th67zIcJ3W0Uf4Iuuvo4qnTZedYiu2lm43uvQwFUXXeN-Tv5X4KLWu95O_usYvD0-bObP2XL1tJjfLrOKSBSzghopKk5RXdKcF0YQVuS1LEurkZWcGc4s4aJgukqvUl5YriUuDSZSUFPmZAxu_ny74L8H20e19UNo00mV04IJTETBk2r2p6qC7_tga9UF1-hwUBipE2CVAKsjYHUCTH4B8j5u0g</recordid><startdate>20150620</startdate><enddate>20150620</enddate><creator>Gu, Guimei</creator><creator>Hu, Rang</creator><creator>Li, Yuanyuan</creator><general>Fuji Technology Press Co. Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20150620</creationdate><title>Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization</title><author>Gu, Guimei ; Hu, Rang ; Li, Yuanyuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c390t-64d98c740fb4276d83562f9bbea0e975d75e37865ac049476e7a91bd13984db23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Acoustic emission</topic><topic>Damage detection</topic><topic>Data collection</topic><topic>Data processing</topic><topic>Particle swarm optimization</topic><topic>Receiving</topic><topic>Signal processing</topic><topic>Support vector machines</topic><topic>Turbine blades</topic><topic>Wind damage</topic><topic>Wind turbines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gu, Guimei</creatorcontrib><creatorcontrib>Hu, Rang</creatorcontrib><creatorcontrib>Li, Yuanyuan</creatorcontrib><creatorcontrib>School of Automation and Electrical Engineering, Lanzhou Jiaotong University</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</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>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</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>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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>Journal of robotics and mechatronics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gu, Guimei</au><au>Hu, Rang</au><au>Li, Yuanyuan</au><aucorp>School of Automation and Electrical Engineering, Lanzhou Jiaotong University</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization</atitle><jtitle>Journal of robotics and mechatronics</jtitle><date>2015-06-20</date><risdate>2015</risdate><volume>27</volume><issue>3</issue><spage>244</spage><epage>250</epage><pages>244-250</pages><issn>0915-3942</issn><eissn>1883-8049</eissn><abstract><div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270003/03.jpg"" width=""340"" />
Classification results of SVM-PSO
In order to identify two failures of crack damage and edge damage to wind turbine blade, a damage identification system was designed by acoustic emission technique. This system took advantage of wireless technique for signal collection and transmission and upper computer for receiving and processing data. This system adopted acoustic emission sensor, NRF905 wireless transmission, upper computer designed by VB language, and the serial communication function of VB for data receiving. Data was firstly normalized after being received. Then, the energy features of data were abstracted by db wavelet. With the abstracted features, support vector machine model was established and verified, and the machine parameters were optimized by particle swarm optimization. Results show that the system is reliable in data collection and transmission, and the correctness of damage identification obviously increases by optimizing the support vector machine with particle swarm. The design provides method to monitor the status of rotating object, so this system can provide model base for subsequent studies.</abstract><cop>Tokyo</cop><pub>Fuji Technology Press Co. Ltd</pub><doi>10.20965/jrm.2015.p0244</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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| source | DOAJ Directory of Open Access Journals; J-STAGE (Japan Science & Technology Information Aggregator, Electronic) Freely Available Titles - Japanese; Open Access Titles of Japan; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Acoustic emission Damage detection Data collection Data processing Particle swarm optimization Receiving Signal processing Support vector machines Turbine blades Wind damage Wind turbines |
| title | Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization |
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