Loading…

Naïve Bayes classifier for temporary short circuit fault detection in stator winding

This paper is proposing Naïve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent shor...

Full description

Saved in:
Bibliographic Details
Main Authors: Asfani, D. A., Purnomo, M. H., Sawitri, D. R.
Format: Conference Proceeding
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 294
container_issue
container_start_page 288
container_title
container_volume
creator Asfani, D. A.
Purnomo, M. H.
Sawitri, D. R.
description This paper is proposing Naïve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent short circuit fault with high impedance. This fault case is representing the early stage of stator insulation break down. The laboratory experiment is performed to simulate the fault cases consist of induction motor with stator modification and current measurement system. The detection system is trained to identify the temporary short circuit occurrence consist of transient starting, steady state and ending of temporary short circuit. The system is also tested using non trained data to clarify the detection performance.
doi_str_mv 10.1109/DEMPED.2013.6645730
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6645730</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6645730</ieee_id><sourcerecordid>6645730</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-8e6ef216a28d7e0dc5e3ce0ceaafd3140550de99b60ab3750fb20997e34cc9943</originalsourceid><addsrcrecordid>eNotj0tOwzAURc0ACShdQSfeQMJzHMf1ENpCkcpnQMfVi_0MRmlS2S6oq2IRbIxKdHRH5-hcxiYCSiHA3MwXT6-LeVmBkGXT1EpLOGNXotbGAFRKX7BxSp8AILQWAPKSrZ_x9-eL-B0eKHHbYUrBB4rcD5Fn2u6GiPHA08cQM7ch2n3I3OO-y9xRJpvD0PPQ85QxH4nv0LvQv1-zc49dovFpR2x9v3ibLYvVy8Pj7HZVBKFVLqbUkK9Eg9XUaQJnFUlLYAnROylqUAocGdM2gK3UCnxbgTGaZG2tMbUcscm_NxDRZhfD9hi7OT2Xf9fkUbI</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Naïve Bayes classifier for temporary short circuit fault detection in stator winding</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Asfani, D. A. ; Purnomo, M. H. ; Sawitri, D. R.</creator><creatorcontrib>Asfani, D. A. ; Purnomo, M. H. ; Sawitri, D. R.</creatorcontrib><description>This paper is proposing Naïve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent short circuit fault with high impedance. This fault case is representing the early stage of stator insulation break down. The laboratory experiment is performed to simulate the fault cases consist of induction motor with stator modification and current measurement system. The detection system is trained to identify the temporary short circuit occurrence consist of transient starting, steady state and ending of temporary short circuit. The system is also tested using non trained data to clarify the detection performance.</description><identifier>EISBN: 1479900257</identifier><identifier>EISBN: 9781479900251</identifier><identifier>DOI: 10.1109/DEMPED.2013.6645730</identifier><language>eng</language><publisher>IEEE</publisher><subject>bayesian methods ; Circuit faults ; Estimation ; Fault detection ; induction motor ; Induction motors ; Kernel ; Stator windings ; stators ; Wavelet transforms</subject><ispartof>2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013, p.288-294</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/6645730$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6645730$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Asfani, D. A.</creatorcontrib><creatorcontrib>Purnomo, M. H.</creatorcontrib><creatorcontrib>Sawitri, D. R.</creatorcontrib><title>Naïve Bayes classifier for temporary short circuit fault detection in stator winding</title><title>2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)</title><addtitle>DEMPED</addtitle><description>This paper is proposing Naïve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent short circuit fault with high impedance. This fault case is representing the early stage of stator insulation break down. The laboratory experiment is performed to simulate the fault cases consist of induction motor with stator modification and current measurement system. The detection system is trained to identify the temporary short circuit occurrence consist of transient starting, steady state and ending of temporary short circuit. The system is also tested using non trained data to clarify the detection performance.</description><subject>bayesian methods</subject><subject>Circuit faults</subject><subject>Estimation</subject><subject>Fault detection</subject><subject>induction motor</subject><subject>Induction motors</subject><subject>Kernel</subject><subject>Stator windings</subject><subject>stators</subject><subject>Wavelet transforms</subject><isbn>1479900257</isbn><isbn>9781479900251</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj0tOwzAURc0ACShdQSfeQMJzHMf1ENpCkcpnQMfVi_0MRmlS2S6oq2IRbIxKdHRH5-hcxiYCSiHA3MwXT6-LeVmBkGXT1EpLOGNXotbGAFRKX7BxSp8AILQWAPKSrZ_x9-eL-B0eKHHbYUrBB4rcD5Fn2u6GiPHA08cQM7ch2n3I3OO-y9xRJpvD0PPQ85QxH4nv0LvQv1-zc49dovFpR2x9v3ibLYvVy8Pj7HZVBKFVLqbUkK9Eg9XUaQJnFUlLYAnROylqUAocGdM2gK3UCnxbgTGaZG2tMbUcscm_NxDRZhfD9hi7OT2Xf9fkUbI</recordid><startdate>201308</startdate><enddate>201308</enddate><creator>Asfani, D. A.</creator><creator>Purnomo, M. H.</creator><creator>Sawitri, D. R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201308</creationdate><title>Naïve Bayes classifier for temporary short circuit fault detection in stator winding</title><author>Asfani, D. A. ; Purnomo, M. H. ; Sawitri, D. R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-8e6ef216a28d7e0dc5e3ce0ceaafd3140550de99b60ab3750fb20997e34cc9943</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>bayesian methods</topic><topic>Circuit faults</topic><topic>Estimation</topic><topic>Fault detection</topic><topic>induction motor</topic><topic>Induction motors</topic><topic>Kernel</topic><topic>Stator windings</topic><topic>stators</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Asfani, D. A.</creatorcontrib><creatorcontrib>Purnomo, M. H.</creatorcontrib><creatorcontrib>Sawitri, D. R.</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/IET 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>Asfani, D. A.</au><au>Purnomo, M. H.</au><au>Sawitri, D. R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Naïve Bayes classifier for temporary short circuit fault detection in stator winding</atitle><btitle>2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)</btitle><stitle>DEMPED</stitle><date>2013-08</date><risdate>2013</risdate><spage>288</spage><epage>294</epage><pages>288-294</pages><eisbn>1479900257</eisbn><eisbn>9781479900251</eisbn><abstract>This paper is proposing Naïve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent short circuit fault with high impedance. This fault case is representing the early stage of stator insulation break down. The laboratory experiment is performed to simulate the fault cases consist of induction motor with stator modification and current measurement system. The detection system is trained to identify the temporary short circuit occurrence consist of transient starting, steady state and ending of temporary short circuit. The system is also tested using non trained data to clarify the detection performance.</abstract><pub>IEEE</pub><doi>10.1109/DEMPED.2013.6645730</doi><tpages>7</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISBN: 1479900257
ispartof 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), 2013, p.288-294
issn
language eng
recordid cdi_ieee_primary_6645730
source IEEE Electronic Library (IEL) Conference Proceedings
subjects bayesian methods
Circuit faults
Estimation
Fault detection
induction motor
Induction motors
Kernel
Stator windings
stators
Wavelet transforms
title Naïve Bayes classifier for temporary short circuit fault detection in stator winding
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T16%3A44%3A02IST&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=Na%C3%AFve%20Bayes%20classifier%20for%20temporary%20short%20circuit%20fault%20detection%20in%20stator%20winding&rft.btitle=2013%209th%20IEEE%20International%20Symposium%20on%20Diagnostics%20for%20Electric%20Machines,%20Power%20Electronics%20and%20Drives%20(SDEMPED)&rft.au=Asfani,%20D.%20A.&rft.date=2013-08&rft.spage=288&rft.epage=294&rft.pages=288-294&rft_id=info:doi/10.1109/DEMPED.2013.6645730&rft.eisbn=1479900257&rft.eisbn_list=9781479900251&rft_dat=%3Cieee_6IE%3E6645730%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-8e6ef216a28d7e0dc5e3ce0ceaafd3140550de99b60ab3750fb20997e34cc9943%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6645730&rfr_iscdi=true