Turn to turn fault diagnosis for induction machines based on wavelet transformation and BP neural network
Based upon Wavelet Transformation analysis and BP neural network, a method for the fault diagnosis of stator winding is proposed in this paper. Firstly wavelet transformation was used to decompose vibration time signal of stator to extract the characteristic values - wavelet transformation energy, a...
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 | 297 |
|---|---|
| container_issue | |
| container_start_page | 294 |
| container_title | |
| container_volume | |
| creator | Najafi, A. Iskender, I. Farhadi, P. Najafi, B. |
| description | Based upon Wavelet Transformation analysis and BP neural network, a method for the fault diagnosis of stator winding is proposed in this paper. Firstly wavelet transformation was used to decompose vibration time signal of stator to extract the characteristic values - wavelet transformation energy, and features were input in to the BP NN. After training the BP NN could be used to identify the stator winding fault (Turn to Turn fault) patterns. Three typical turn to turn faults as 10 turn, 20 turn and 35 turn were studied. The result showed that the method of BP NN with wavelet transformation could not only detect the exiting of the fault in stator winding, but also effectively identify the fault patterns. |
| doi_str_mv | 10.1109/ACEMP.2011.6490613 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6490613</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6490613</ieee_id><sourcerecordid>6490613</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-651a78e5538cb3554c2570dac118c8516bd07a29b20a92a4fc0cda50da9509003</originalsourceid><addsrcrecordid>eNpFkMtOwzAQRY0QElD6A7DxD6SME9uJlyUqD6mILsq6mtgOGBIH2Q4Vf0-ASqyO7ujoSncIuWSwYAzU9bJePW4WOTC2kFyBZMUROWdcloUAKOTxf-D8lMxjfAOYZJAVr86I247B0zTQ9MMWxy5R4_DFD9FF2g6BOm9GndzgaY_61XkbaYPRGjpd9vhpO5toCujjJPf4K6I39GZDvR0DdhPSfgjvF-SkxS7a-YEz8ny72tb32frp7qFerjPHSpEyKRiWlRWiqHRTCMF1LkowqBmrdCWYbAyUmKsmB1Q58laDNigmQwlQ0-IZufrrddba3UdwPYav3eE1xTek7VkF</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Turn to turn fault diagnosis for induction machines based on wavelet transformation and BP neural network</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Najafi, A. ; Iskender, I. ; Farhadi, P. ; Najafi, B.</creator><creatorcontrib>Najafi, A. ; Iskender, I. ; Farhadi, P. ; Najafi, B.</creatorcontrib><description>Based upon Wavelet Transformation analysis and BP neural network, a method for the fault diagnosis of stator winding is proposed in this paper. Firstly wavelet transformation was used to decompose vibration time signal of stator to extract the characteristic values - wavelet transformation energy, and features were input in to the BP NN. After training the BP NN could be used to identify the stator winding fault (Turn to Turn fault) patterns. Three typical turn to turn faults as 10 turn, 20 turn and 35 turn were studied. The result showed that the method of BP NN with wavelet transformation could not only detect the exiting of the fault in stator winding, but also effectively identify the fault patterns.</description><identifier>ISBN: 1467350044</identifier><identifier>ISBN: 9781467350044</identifier><identifier>EISBN: 1467350036</identifier><identifier>EISBN: 9781467350020</identifier><identifier>EISBN: 9781467350037</identifier><identifier>EISBN: 1467350028</identifier><identifier>DOI: 10.1109/ACEMP.2011.6490613</identifier><language>eng</language><publisher>IEEE</publisher><subject>BP network ; Fault diagnosis ; Stator winding ; Wavelet Transformation</subject><ispartof>International Aegean Conference on Electrical Machines and Power Electronics and Electromotion, Joint Conference, 2011, p.294-297</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/6490613$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,27930,54925</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6490613$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Najafi, A.</creatorcontrib><creatorcontrib>Iskender, I.</creatorcontrib><creatorcontrib>Farhadi, P.</creatorcontrib><creatorcontrib>Najafi, B.</creatorcontrib><title>Turn to turn fault diagnosis for induction machines based on wavelet transformation and BP neural network</title><title>International Aegean Conference on Electrical Machines and Power Electronics and Electromotion, Joint Conference</title><addtitle>ACEMP</addtitle><description>Based upon Wavelet Transformation analysis and BP neural network, a method for the fault diagnosis of stator winding is proposed in this paper. Firstly wavelet transformation was used to decompose vibration time signal of stator to extract the characteristic values - wavelet transformation energy, and features were input in to the BP NN. After training the BP NN could be used to identify the stator winding fault (Turn to Turn fault) patterns. Three typical turn to turn faults as 10 turn, 20 turn and 35 turn were studied. The result showed that the method of BP NN with wavelet transformation could not only detect the exiting of the fault in stator winding, but also effectively identify the fault patterns.</description><subject>BP network</subject><subject>Fault diagnosis</subject><subject>Stator winding</subject><subject>Wavelet Transformation</subject><isbn>1467350044</isbn><isbn>9781467350044</isbn><isbn>1467350036</isbn><isbn>9781467350020</isbn><isbn>9781467350037</isbn><isbn>1467350028</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkMtOwzAQRY0QElD6A7DxD6SME9uJlyUqD6mILsq6mtgOGBIH2Q4Vf0-ASqyO7ujoSncIuWSwYAzU9bJePW4WOTC2kFyBZMUROWdcloUAKOTxf-D8lMxjfAOYZJAVr86I247B0zTQ9MMWxy5R4_DFD9FF2g6BOm9GndzgaY_61XkbaYPRGjpd9vhpO5toCujjJPf4K6I39GZDvR0DdhPSfgjvF-SkxS7a-YEz8ny72tb32frp7qFerjPHSpEyKRiWlRWiqHRTCMF1LkowqBmrdCWYbAyUmKsmB1Q58laDNigmQwlQ0-IZufrrddba3UdwPYav3eE1xTek7VkF</recordid><startdate>201109</startdate><enddate>201109</enddate><creator>Najafi, A.</creator><creator>Iskender, I.</creator><creator>Farhadi, P.</creator><creator>Najafi, B.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201109</creationdate><title>Turn to turn fault diagnosis for induction machines based on wavelet transformation and BP neural network</title><author>Najafi, A. ; Iskender, I. ; Farhadi, P. ; Najafi, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-651a78e5538cb3554c2570dac118c8516bd07a29b20a92a4fc0cda50da9509003</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>BP network</topic><topic>Fault diagnosis</topic><topic>Stator winding</topic><topic>Wavelet Transformation</topic><toplevel>online_resources</toplevel><creatorcontrib>Najafi, A.</creatorcontrib><creatorcontrib>Iskender, I.</creatorcontrib><creatorcontrib>Farhadi, P.</creatorcontrib><creatorcontrib>Najafi, B.</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>Najafi, A.</au><au>Iskender, I.</au><au>Farhadi, P.</au><au>Najafi, B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Turn to turn fault diagnosis for induction machines based on wavelet transformation and BP neural network</atitle><btitle>International Aegean Conference on Electrical Machines and Power Electronics and Electromotion, Joint Conference</btitle><stitle>ACEMP</stitle><date>2011-09</date><risdate>2011</risdate><spage>294</spage><epage>297</epage><pages>294-297</pages><isbn>1467350044</isbn><isbn>9781467350044</isbn><eisbn>1467350036</eisbn><eisbn>9781467350020</eisbn><eisbn>9781467350037</eisbn><eisbn>1467350028</eisbn><abstract>Based upon Wavelet Transformation analysis and BP neural network, a method for the fault diagnosis of stator winding is proposed in this paper. Firstly wavelet transformation was used to decompose vibration time signal of stator to extract the characteristic values - wavelet transformation energy, and features were input in to the BP NN. After training the BP NN could be used to identify the stator winding fault (Turn to Turn fault) patterns. Three typical turn to turn faults as 10 turn, 20 turn and 35 turn were studied. The result showed that the method of BP NN with wavelet transformation could not only detect the exiting of the fault in stator winding, but also effectively identify the fault patterns.</abstract><pub>IEEE</pub><doi>10.1109/ACEMP.2011.6490613</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 1467350044 |
| ispartof | International Aegean Conference on Electrical Machines and Power Electronics and Electromotion, Joint Conference, 2011, p.294-297 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_6490613 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | BP network Fault diagnosis Stator winding Wavelet Transformation |
| title | Turn to turn fault diagnosis for induction machines based on wavelet transformation and BP neural network |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-14T01%3A20%3A14IST&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=Turn%20to%20turn%20fault%20diagnosis%20for%20induction%20machines%20based%20on%20wavelet%20transformation%20and%20BP%20neural%20network&rft.btitle=International%20Aegean%20Conference%20on%20Electrical%20Machines%20and%20Power%20Electronics%20and%20Electromotion,%20Joint%20Conference&rft.au=Najafi,%20A.&rft.date=2011-09&rft.spage=294&rft.epage=297&rft.pages=294-297&rft.isbn=1467350044&rft.isbn_list=9781467350044&rft_id=info:doi/10.1109/ACEMP.2011.6490613&rft_dat=%3Cieee_6IE%3E6490613%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1467350036&rft.eisbn_list=9781467350020&rft.eisbn_list=9781467350037&rft.eisbn_list=1467350028&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6490613&rfr_iscdi=true |