Application of RBF neural network in fault diagnosis of FOG SINS
Taking FOG SINS (fiber-optic gyroscope strapdown inertial system) as an object, a new fault diagnostic scheme based on RBF(radial basis function) neural network is proposed. Being capable of training and simulating data off-line, neural networks provide a solution to overcome some drawbacks of the q...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1035 |
|---|---|
| container_issue | |
| container_start_page | 1032 |
| container_title | |
| container_volume | |
| creator | Wu Lei Sun Rong-Ping Cheng Jian-Hua |
| description | Taking FOG SINS (fiber-optic gyroscope strapdown inertial system) as an object, a new fault diagnostic scheme based on RBF(radial basis function) neural network is proposed. Being capable of training and simulating data off-line, neural networks provide a solution to overcome some drawbacks of the quantitative fault diagnosis. The fault tree of FOG SINS is analyzed, which is the basis of the study of neural network fault diagnosis technology. The structure and inferential mechanism of RBF network used for elementary fault diagnosis are discussed in detail. Training simulation results of the neural network are given and an improved effect with real data is obtained, which show the feasibility of the proposed scheme. |
| doi_str_mv | 10.1109/ICCAS.2008.4694651 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4694651</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4694651</ieee_id><sourcerecordid>4694651</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-37a71069df1e89d4ece429a3390a73a7dc76b539d56cc490239aac3b6452405d3</originalsourceid><addsrcrecordid>eNotj81Kw0AURkdEUGtfQDfzAol3Mj_J3RmDqYFiwXRfbmcmMhqTkKSIb2_Vrg4fHD44jN0KiIUAvK-KIq_jBCCLlUFltDhj1xmiTIQGoc7_hgaQGWaXbDlN7wAg0KRCmCv2kA9DGyzNoe943_DXx5J3_jBSe8T81Y8fPHS8oUM7cxforeunMP2K5WbF6-qlvmEXDbWTX564YNvyaVs8R-vNqirydRQQ5kimlAow6BrhM3TKW68SJCkRKJWUOpuavZbotLFWISQSiazcG6UTBdrJBbv7vw3e-90whk8av3enYPkDOJVIAw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Application of RBF neural network in fault diagnosis of FOG SINS</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Wu Lei ; Sun Rong-Ping ; Cheng Jian-Hua</creator><creatorcontrib>Wu Lei ; Sun Rong-Ping ; Cheng Jian-Hua</creatorcontrib><description>Taking FOG SINS (fiber-optic gyroscope strapdown inertial system) as an object, a new fault diagnostic scheme based on RBF(radial basis function) neural network is proposed. Being capable of training and simulating data off-line, neural networks provide a solution to overcome some drawbacks of the quantitative fault diagnosis. The fault tree of FOG SINS is analyzed, which is the basis of the study of neural network fault diagnosis technology. The structure and inferential mechanism of RBF network used for elementary fault diagnosis are discussed in detail. Training simulation results of the neural network are given and an improved effect with real data is obtained, which show the feasibility of the proposed scheme.</description><identifier>ISBN: 8995003898</identifier><identifier>ISBN: 9788995003893</identifier><identifier>EISBN: 8993215014</identifier><identifier>EISBN: 9788993215014</identifier><identifier>DOI: 10.1109/ICCAS.2008.4694651</identifier><language>eng</language><publisher>IEEE</publisher><subject>Automatic control ; Automation ; Circuit faults ; Control systems ; Digital signal processing ; Fault diagnosis ; Fault trees ; FOG SINS ; Neural networks ; Radial basis function networks ; RBF neural network ; Silicon compounds</subject><ispartof>2008 International Conference on Control, Automation and Systems, 2008, p.1032-1035</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4694651$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4694651$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wu Lei</creatorcontrib><creatorcontrib>Sun Rong-Ping</creatorcontrib><creatorcontrib>Cheng Jian-Hua</creatorcontrib><title>Application of RBF neural network in fault diagnosis of FOG SINS</title><title>2008 International Conference on Control, Automation and Systems</title><addtitle>ICCAS</addtitle><description>Taking FOG SINS (fiber-optic gyroscope strapdown inertial system) as an object, a new fault diagnostic scheme based on RBF(radial basis function) neural network is proposed. Being capable of training and simulating data off-line, neural networks provide a solution to overcome some drawbacks of the quantitative fault diagnosis. The fault tree of FOG SINS is analyzed, which is the basis of the study of neural network fault diagnosis technology. The structure and inferential mechanism of RBF network used for elementary fault diagnosis are discussed in detail. Training simulation results of the neural network are given and an improved effect with real data is obtained, which show the feasibility of the proposed scheme.</description><subject>Automatic control</subject><subject>Automation</subject><subject>Circuit faults</subject><subject>Control systems</subject><subject>Digital signal processing</subject><subject>Fault diagnosis</subject><subject>Fault trees</subject><subject>FOG SINS</subject><subject>Neural networks</subject><subject>Radial basis function networks</subject><subject>RBF neural network</subject><subject>Silicon compounds</subject><isbn>8995003898</isbn><isbn>9788995003893</isbn><isbn>8993215014</isbn><isbn>9788993215014</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj81Kw0AURkdEUGtfQDfzAol3Mj_J3RmDqYFiwXRfbmcmMhqTkKSIb2_Vrg4fHD44jN0KiIUAvK-KIq_jBCCLlUFltDhj1xmiTIQGoc7_hgaQGWaXbDlN7wAg0KRCmCv2kA9DGyzNoe943_DXx5J3_jBSe8T81Y8fPHS8oUM7cxforeunMP2K5WbF6-qlvmEXDbWTX564YNvyaVs8R-vNqirydRQQ5kimlAow6BrhM3TKW68SJCkRKJWUOpuavZbotLFWISQSiazcG6UTBdrJBbv7vw3e-90whk8av3enYPkDOJVIAw</recordid><startdate>200810</startdate><enddate>200810</enddate><creator>Wu Lei</creator><creator>Sun Rong-Ping</creator><creator>Cheng Jian-Hua</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200810</creationdate><title>Application of RBF neural network in fault diagnosis of FOG SINS</title><author>Wu Lei ; Sun Rong-Ping ; Cheng Jian-Hua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-37a71069df1e89d4ece429a3390a73a7dc76b539d56cc490239aac3b6452405d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Automatic control</topic><topic>Automation</topic><topic>Circuit faults</topic><topic>Control systems</topic><topic>Digital signal processing</topic><topic>Fault diagnosis</topic><topic>Fault trees</topic><topic>FOG SINS</topic><topic>Neural networks</topic><topic>Radial basis function networks</topic><topic>RBF neural network</topic><topic>Silicon compounds</topic><toplevel>online_resources</toplevel><creatorcontrib>Wu Lei</creatorcontrib><creatorcontrib>Sun Rong-Ping</creatorcontrib><creatorcontrib>Cheng Jian-Hua</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wu Lei</au><au>Sun Rong-Ping</au><au>Cheng Jian-Hua</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Application of RBF neural network in fault diagnosis of FOG SINS</atitle><btitle>2008 International Conference on Control, Automation and Systems</btitle><stitle>ICCAS</stitle><date>2008-10</date><risdate>2008</risdate><spage>1032</spage><epage>1035</epage><pages>1032-1035</pages><isbn>8995003898</isbn><isbn>9788995003893</isbn><eisbn>8993215014</eisbn><eisbn>9788993215014</eisbn><abstract>Taking FOG SINS (fiber-optic gyroscope strapdown inertial system) as an object, a new fault diagnostic scheme based on RBF(radial basis function) neural network is proposed. Being capable of training and simulating data off-line, neural networks provide a solution to overcome some drawbacks of the quantitative fault diagnosis. The fault tree of FOG SINS is analyzed, which is the basis of the study of neural network fault diagnosis technology. The structure and inferential mechanism of RBF network used for elementary fault diagnosis are discussed in detail. Training simulation results of the neural network are given and an improved effect with real data is obtained, which show the feasibility of the proposed scheme.</abstract><pub>IEEE</pub><doi>10.1109/ICCAS.2008.4694651</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 8995003898 |
| ispartof | 2008 International Conference on Control, Automation and Systems, 2008, p.1032-1035 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_4694651 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Automatic control Automation Circuit faults Control systems Digital signal processing Fault diagnosis Fault trees FOG SINS Neural networks Radial basis function networks RBF neural network Silicon compounds |
| title | Application of RBF neural network in fault diagnosis of FOG SINS |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T13%3A06%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Application%20of%20RBF%20neural%20network%20in%20fault%20diagnosis%20of%20FOG%20SINS&rft.btitle=2008%20International%20Conference%20on%20Control,%20Automation%20and%20Systems&rft.au=Wu%20Lei&rft.date=2008-10&rft.spage=1032&rft.epage=1035&rft.pages=1032-1035&rft.isbn=8995003898&rft.isbn_list=9788995003893&rft_id=info:doi/10.1109/ICCAS.2008.4694651&rft_dat=%3Cieee_6IE%3E4694651%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=8993215014&rft.eisbn_list=9788993215014&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4694651&rfr_iscdi=true |