Fault diagnosis for gear pump based on feature fusion of vibration signal
Information fusion arises in a surprising number of fault diagnosis applications. In this paper, common faults are designed in the experiment according to the gear pump vibration mechanism. Fault signal is collected from vibration sensors of different positions, and wavelet packet energy percentage...
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 | 712 |
|---|---|
| container_issue | |
| container_start_page | 709 |
| container_title | |
| container_volume | |
| creator | Xiliang Liu Guiming Chen Fangxi Li Qian Zhang Zhenqi Dong |
| description | Information fusion arises in a surprising number of fault diagnosis applications. In this paper, common faults are designed in the experiment according to the gear pump vibration mechanism. Fault signal is collected from vibration sensors of different positions, and wavelet packet energy percentage and RMS are extracted as features of the signal. RBF neural network is adopted to fuse thiese features which are used to learn and train the network. The testing results prove that this approach possesses higher diagnostic precision and better diagnostic effect than single signal fault diagnosis. |
| doi_str_mv | 10.1109/ICQR2MSE.2012.6246328 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6246328</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6246328</ieee_id><sourcerecordid>6246328</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-d6ee655f3e6c7d0737d0fb0191584a9a9b5696143188396b954b92815c4790e33</originalsourceid><addsrcrecordid>eNpVkM1KxDAcxCMiKGufQIS8QNf8852jlF0trIhf5yXZJiXSbUvSCr69FffiHGb4zWEOg9AtkDUAMXd19fJKn942a0qAriXlklF9hgqjNHCpGFFawfk_lvwSFTl_kkVLSzm5QvXWzt2Em2jbfsgx4zAk3Hqb8DgfR-xs9g0eehy8nebkcZhzXHAI-Cu6ZKdfyLHtbXeNLoLtsi9OuUIf28179Vjunh_q6n5XRlBiKhvpvRQiMC8PqiGKLRYcAQNCc2uscUIaCZyB1sxIZwR3hmoQB64M8Yyt0M3fbvTe78cUjzZ9708HsB-Un043</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Fault diagnosis for gear pump based on feature fusion of vibration signal</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Xiliang Liu ; Guiming Chen ; Fangxi Li ; Qian Zhang ; Zhenqi Dong</creator><creatorcontrib>Xiliang Liu ; Guiming Chen ; Fangxi Li ; Qian Zhang ; Zhenqi Dong</creatorcontrib><description>Information fusion arises in a surprising number of fault diagnosis applications. In this paper, common faults are designed in the experiment according to the gear pump vibration mechanism. Fault signal is collected from vibration sensors of different positions, and wavelet packet energy percentage and RMS are extracted as features of the signal. RBF neural network is adopted to fuse thiese features which are used to learn and train the network. The testing results prove that this approach possesses higher diagnostic precision and better diagnostic effect than single signal fault diagnosis.</description><identifier>ISBN: 9781467307864</identifier><identifier>ISBN: 1467307866</identifier><identifier>EISBN: 9781467307871</identifier><identifier>EISBN: 9781467307888</identifier><identifier>EISBN: 1467307882</identifier><identifier>EISBN: 1467307874</identifier><identifier>DOI: 10.1109/ICQR2MSE.2012.6246328</identifier><language>eng</language><publisher>IEEE</publisher><subject>Fault diagnosis ; feature fusion ; Gears ; neural network ; Neural networks ; Pumps ; Testing ; Vibrations ; wavelet packet energy percentage ; Wavelet packets</subject><ispartof>2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, 2012, p.709-712</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/6246328$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6246328$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Xiliang Liu</creatorcontrib><creatorcontrib>Guiming Chen</creatorcontrib><creatorcontrib>Fangxi Li</creatorcontrib><creatorcontrib>Qian Zhang</creatorcontrib><creatorcontrib>Zhenqi Dong</creatorcontrib><title>Fault diagnosis for gear pump based on feature fusion of vibration signal</title><title>2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering</title><addtitle>ICQR2MSE</addtitle><description>Information fusion arises in a surprising number of fault diagnosis applications. In this paper, common faults are designed in the experiment according to the gear pump vibration mechanism. Fault signal is collected from vibration sensors of different positions, and wavelet packet energy percentage and RMS are extracted as features of the signal. RBF neural network is adopted to fuse thiese features which are used to learn and train the network. The testing results prove that this approach possesses higher diagnostic precision and better diagnostic effect than single signal fault diagnosis.</description><subject>Fault diagnosis</subject><subject>feature fusion</subject><subject>Gears</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Pumps</subject><subject>Testing</subject><subject>Vibrations</subject><subject>wavelet packet energy percentage</subject><subject>Wavelet packets</subject><isbn>9781467307864</isbn><isbn>1467307866</isbn><isbn>9781467307871</isbn><isbn>9781467307888</isbn><isbn>1467307882</isbn><isbn>1467307874</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkM1KxDAcxCMiKGufQIS8QNf8852jlF0trIhf5yXZJiXSbUvSCr69FffiHGb4zWEOg9AtkDUAMXd19fJKn942a0qAriXlklF9hgqjNHCpGFFawfk_lvwSFTl_kkVLSzm5QvXWzt2Em2jbfsgx4zAk3Hqb8DgfR-xs9g0eehy8nebkcZhzXHAI-Cu6ZKdfyLHtbXeNLoLtsi9OuUIf28179Vjunh_q6n5XRlBiKhvpvRQiMC8PqiGKLRYcAQNCc2uscUIaCZyB1sxIZwR3hmoQB64M8Yyt0M3fbvTe78cUjzZ9708HsB-Un043</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>Xiliang Liu</creator><creator>Guiming Chen</creator><creator>Fangxi Li</creator><creator>Qian Zhang</creator><creator>Zhenqi Dong</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201206</creationdate><title>Fault diagnosis for gear pump based on feature fusion of vibration signal</title><author>Xiliang Liu ; Guiming Chen ; Fangxi Li ; Qian Zhang ; Zhenqi Dong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-d6ee655f3e6c7d0737d0fb0191584a9a9b5696143188396b954b92815c4790e33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Fault diagnosis</topic><topic>feature fusion</topic><topic>Gears</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Pumps</topic><topic>Testing</topic><topic>Vibrations</topic><topic>wavelet packet energy percentage</topic><topic>Wavelet packets</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiliang Liu</creatorcontrib><creatorcontrib>Guiming Chen</creatorcontrib><creatorcontrib>Fangxi Li</creatorcontrib><creatorcontrib>Qian Zhang</creatorcontrib><creatorcontrib>Zhenqi Dong</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>Xiliang Liu</au><au>Guiming Chen</au><au>Fangxi Li</au><au>Qian Zhang</au><au>Zhenqi Dong</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fault diagnosis for gear pump based on feature fusion of vibration signal</atitle><btitle>2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering</btitle><stitle>ICQR2MSE</stitle><date>2012-06</date><risdate>2012</risdate><spage>709</spage><epage>712</epage><pages>709-712</pages><isbn>9781467307864</isbn><isbn>1467307866</isbn><eisbn>9781467307871</eisbn><eisbn>9781467307888</eisbn><eisbn>1467307882</eisbn><eisbn>1467307874</eisbn><abstract>Information fusion arises in a surprising number of fault diagnosis applications. In this paper, common faults are designed in the experiment according to the gear pump vibration mechanism. Fault signal is collected from vibration sensors of different positions, and wavelet packet energy percentage and RMS are extracted as features of the signal. RBF neural network is adopted to fuse thiese features which are used to learn and train the network. The testing results prove that this approach possesses higher diagnostic precision and better diagnostic effect than single signal fault diagnosis.</abstract><pub>IEEE</pub><doi>10.1109/ICQR2MSE.2012.6246328</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781467307864 |
| ispartof | 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, 2012, p.709-712 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_6246328 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Fault diagnosis feature fusion Gears neural network Neural networks Pumps Testing Vibrations wavelet packet energy percentage Wavelet packets |
| title | Fault diagnosis for gear pump based on feature fusion of vibration signal |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T04%3A40%3A49IST&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=Fault%20diagnosis%20for%20gear%20pump%20based%20on%20feature%20fusion%20of%20vibration%20signal&rft.btitle=2012%20International%20Conference%20on%20Quality,%20Reliability,%20Risk,%20Maintenance,%20and%20Safety%20Engineering&rft.au=Xiliang%20Liu&rft.date=2012-06&rft.spage=709&rft.epage=712&rft.pages=709-712&rft.isbn=9781467307864&rft.isbn_list=1467307866&rft_id=info:doi/10.1109/ICQR2MSE.2012.6246328&rft_dat=%3Cieee_6IE%3E6246328%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781467307871&rft.eisbn_list=9781467307888&rft.eisbn_list=1467307882&rft.eisbn_list=1467307874&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6246328&rfr_iscdi=true |