Composite Fault Diagnosis of Aviation Generator Based on EnFWA-DBN
Because of the existence of composite faults, which consist of both short-out and eccentricity faults, the characteristics of the output voltage and internal magnetic field of aviation generators are less different than those of single short-out faults; this causes the eccentricity fault to be diffi...
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| Veröffentlicht in: | Processes 2023-05, Vol.11 (5), p.1577 |
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| creator | Yang, Zhangang Bao, Xingwang Zhou, Qingyu Yang, Juan |
| description | Because of the existence of composite faults, which consist of both short-out and eccentricity faults, the characteristics of the output voltage and internal magnetic field of aviation generators are less different than those of single short-out faults; this causes the eccentricity fault to be difficult to identify. In order to solve this problem, this paper proposes a fault diagnosis method using an enhanced fireworks algorithm (EnFWA) to optimize a deep belief network (DBN). The aviation generator model is built according to the finite element method (FEM), whereas the output of different combinations of composite faults are obtained using simulations. The EnFWA algorithm is used to train and optimize the DBN network to obtain the best structure. Meanwhile, an extreme learning machine (ELM) classifier performs fault diagnosis and classification on the test data. The diagnosis results show that a pinpoint accuracy can be achieved using the proposed method in the diagnosis of composite faults in aviation generators. |
| doi_str_mv | 10.3390/pr11051577 |
| format | Article |
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In order to solve this problem, this paper proposes a fault diagnosis method using an enhanced fireworks algorithm (EnFWA) to optimize a deep belief network (DBN). The aviation generator model is built according to the finite element method (FEM), whereas the output of different combinations of composite faults are obtained using simulations. The EnFWA algorithm is used to train and optimize the DBN network to obtain the best structure. Meanwhile, an extreme learning machine (ELM) classifier performs fault diagnosis and classification on the test data. The diagnosis results show that a pinpoint accuracy can be achieved using the proposed method in the diagnosis of composite faults in aviation generators.</description><identifier>ISSN: 2227-9717</identifier><identifier>EISSN: 2227-9717</identifier><identifier>DOI: 10.3390/pr11051577</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Aeronautics ; Algorithms ; Analysis ; Artificial neural networks ; Aviation ; Belief networks ; Eccentricity ; Fault detection ; Fault diagnosis ; Faults ; Finite element method ; Fireworks ; Machine learning ; Magnetic fields ; Permeability ; Power supply ; Spectrum analysis</subject><ispartof>Processes, 2023-05, Vol.11 (5), p.1577</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-c334t-8a5ff82e5a899cba88db678dc30d9147bb9b408311a6487fc4b8fc3d5a954f103</citedby><cites>FETCH-LOGICAL-c334t-8a5ff82e5a899cba88db678dc30d9147bb9b408311a6487fc4b8fc3d5a954f103</cites><orcidid>0000-0001-6837-6889 ; 0000-0001-8918-2872</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Yang, Zhangang</creatorcontrib><creatorcontrib>Bao, Xingwang</creatorcontrib><creatorcontrib>Zhou, Qingyu</creatorcontrib><creatorcontrib>Yang, Juan</creatorcontrib><title>Composite Fault Diagnosis of Aviation Generator Based on EnFWA-DBN</title><title>Processes</title><description>Because of the existence of composite faults, which consist of both short-out and eccentricity faults, the characteristics of the output voltage and internal magnetic field of aviation generators are less different than those of single short-out faults; this causes the eccentricity fault to be difficult to identify. In order to solve this problem, this paper proposes a fault diagnosis method using an enhanced fireworks algorithm (EnFWA) to optimize a deep belief network (DBN). The aviation generator model is built according to the finite element method (FEM), whereas the output of different combinations of composite faults are obtained using simulations. The EnFWA algorithm is used to train and optimize the DBN network to obtain the best structure. Meanwhile, an extreme learning machine (ELM) classifier performs fault diagnosis and classification on the test data. The diagnosis results show that a pinpoint accuracy can be achieved using the proposed method in the diagnosis of composite faults in aviation generators.</description><subject>Aeronautics</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial neural networks</subject><subject>Aviation</subject><subject>Belief networks</subject><subject>Eccentricity</subject><subject>Fault detection</subject><subject>Fault diagnosis</subject><subject>Faults</subject><subject>Finite element method</subject><subject>Fireworks</subject><subject>Machine learning</subject><subject>Magnetic fields</subject><subject>Permeability</subject><subject>Power supply</subject><subject>Spectrum analysis</subject><issn>2227-9717</issn><issn>2227-9717</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><sourceid>GNUQQ</sourceid><recordid>eNpNkE9LAzEQxYMoWGovfoKAN2Frskk2yXH7V6HoRfG4ZLNJSWmTNdkV_PZGKujMYYYf783AA-AWozkhEj30EWPEMOP8AkzKsuSF5Jhf_tuvwSylA8olMRGsmoDFMpz6kNxg4EaNxwGunNr7DBIMFtafTg0ueLg13kQ1hAgXKpkOZrT2m_e6WC2eb8CVVcdkZr9zCt4269flY7F72T4t612hCaFDIRSzVpSGKSGlbpUQXVtx0WmCOokpb1vZUiQIxqqigltNW2E16ZiSjFqMyBTcne_2MXyMJg3NIYzR55dNKbCkjCLOsmp-Vu3V0TTO2zBEpXN35uR08Ma6zGvOkJS0kjQb7s8GHUNK0dimj-6k4leDUfOTa_OXK_kGnKdn-g</recordid><startdate>20230522</startdate><enddate>20230522</enddate><creator>Yang, Zhangang</creator><creator>Bao, Xingwang</creator><creator>Zhou, Qingyu</creator><creator>Yang, Juan</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>LK8</scope><scope>M7P</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-6837-6889</orcidid><orcidid>https://orcid.org/0000-0001-8918-2872</orcidid></search><sort><creationdate>20230522</creationdate><title>Composite Fault Diagnosis of Aviation Generator Based on EnFWA-DBN</title><author>Yang, Zhangang ; Bao, Xingwang ; Zhou, Qingyu ; Yang, Juan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-8a5ff82e5a899cba88db678dc30d9147bb9b408311a6487fc4b8fc3d5a954f103</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Aeronautics</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Artificial neural networks</topic><topic>Aviation</topic><topic>Belief networks</topic><topic>Eccentricity</topic><topic>Fault detection</topic><topic>Fault diagnosis</topic><topic>Faults</topic><topic>Finite element method</topic><topic>Fireworks</topic><topic>Machine learning</topic><topic>Magnetic fields</topic><topic>Permeability</topic><topic>Power supply</topic><topic>Spectrum analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Zhangang</creatorcontrib><creatorcontrib>Bao, Xingwang</creatorcontrib><creatorcontrib>Zhou, Qingyu</creatorcontrib><creatorcontrib>Yang, Juan</creatorcontrib><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>ProQuest Biological Science Collection</collection><collection>Biological Science Database</collection><collection>Materials Science 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>Processes</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Zhangang</au><au>Bao, Xingwang</au><au>Zhou, Qingyu</au><au>Yang, Juan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Composite Fault Diagnosis of Aviation Generator Based on EnFWA-DBN</atitle><jtitle>Processes</jtitle><date>2023-05-22</date><risdate>2023</risdate><volume>11</volume><issue>5</issue><spage>1577</spage><pages>1577-</pages><issn>2227-9717</issn><eissn>2227-9717</eissn><abstract>Because of the existence of composite faults, which consist of both short-out and eccentricity faults, the characteristics of the output voltage and internal magnetic field of aviation generators are less different than those of single short-out faults; this causes the eccentricity fault to be difficult to identify. In order to solve this problem, this paper proposes a fault diagnosis method using an enhanced fireworks algorithm (EnFWA) to optimize a deep belief network (DBN). The aviation generator model is built according to the finite element method (FEM), whereas the output of different combinations of composite faults are obtained using simulations. The EnFWA algorithm is used to train and optimize the DBN network to obtain the best structure. Meanwhile, an extreme learning machine (ELM) classifier performs fault diagnosis and classification on the test data. The diagnosis results show that a pinpoint accuracy can be achieved using the proposed method in the diagnosis of composite faults in aviation generators.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/pr11051577</doi><orcidid>https://orcid.org/0000-0001-6837-6889</orcidid><orcidid>https://orcid.org/0000-0001-8918-2872</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aeronautics Algorithms Analysis Artificial neural networks Aviation Belief networks Eccentricity Fault detection Fault diagnosis Faults Finite element method Fireworks Machine learning Magnetic fields Permeability Power supply Spectrum analysis |
| title | Composite Fault Diagnosis of Aviation Generator Based on EnFWA-DBN |
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