Loading…
Advanced Signal Processing Techniques for Bearing Fault Detection in Induction Motors
This paper focuses on bearing fault (BF) detection in induction motors based on a combination of advanced signal processing tools with motor current signature analysis (MCSA). The applied tools consist on two main techniques: the power spectral density (PSD) estimation for spectral analysis and wave...
Saved in:
Main Authors: | , |
---|---|
Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Citations: | Items that cite this one |
Online Access: | Request full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c222t-3c4ba6149201273873d791e75c6f3c2e9ecdcd863fbe24e5f92b6a1ec91e64613 |
---|---|
cites | |
container_end_page | 887 |
container_issue | |
container_start_page | 882 |
container_title | |
container_volume | |
creator | Ben Abid, Firas Braham, Ahmed |
description | This paper focuses on bearing fault (BF) detection in induction motors based on a combination of advanced signal processing tools with motor current signature analysis (MCSA). The applied tools consist on two main techniques: the power spectral density (PSD) estimation for spectral analysis and wavelet techniques for time-frequency analysis. In order to identify the signatures related to bearing faults in the current spectrum, the Welch and the Multiple Signal Classification (MUSIC) PSD estimations are applied. Since the PSD techniques are not appropriate for non-stationary conditions, the Discrete Wavelet Transform (DWT), the Wavelet Packet Transform (WPT) and the Stationary WPT (SWPT) are used and compared. The efficiency of the proposed approaches is verified by several experiments corresponding to three types of bearing faults. The wavelet analysis, especially the SWPT, shows its ability to identify the BF signatures more accurately than other wavelet techniques regardless the load level. |
doi_str_mv | 10.1109/SSD.2018.8570403 |
format | conference_proceeding |
fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_8570403</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8570403</ieee_id><sourcerecordid>8570403</sourcerecordid><originalsourceid>FETCH-LOGICAL-c222t-3c4ba6149201273873d791e75c6f3c2e9ecdcd863fbe24e5f92b6a1ec91e64613</originalsourceid><addsrcrecordid>eNotkE1LAzEYhKMgWGvvgpf8ga35_jjW1mqhorDtuWSTd2tkzepmV-i_t9KehhkehmEQuqNkSimxD2W5mDJCzdRITQThF-iGSm6U5ESySzRiQouCCKGu0STnT0IIU4ZrK0ZoOwu_LnkIuIz75Br83rUeco5pjzfgP1L8GSDjuu3wI7juP166oenxAnrwfWwTjgmvUhhO5rXt2y7foqvaNRkmZx2j7fJpM38p1m_Pq_lsXXjGWF9wLyqnqLDH8Uxzo3nQloKWXtXcM7Dggw9G8boCJkDWllXKUfBHSAlF-Rjdn3ojAOy-u_jlusPu_AL_A8hNUMc</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Advanced Signal Processing Techniques for Bearing Fault Detection in Induction Motors</title><source>IEEE Xplore All Conference Series</source><creator>Ben Abid, Firas ; Braham, Ahmed</creator><creatorcontrib>Ben Abid, Firas ; Braham, Ahmed</creatorcontrib><description>This paper focuses on bearing fault (BF) detection in induction motors based on a combination of advanced signal processing tools with motor current signature analysis (MCSA). The applied tools consist on two main techniques: the power spectral density (PSD) estimation for spectral analysis and wavelet techniques for time-frequency analysis. In order to identify the signatures related to bearing faults in the current spectrum, the Welch and the Multiple Signal Classification (MUSIC) PSD estimations are applied. Since the PSD techniques are not appropriate for non-stationary conditions, the Discrete Wavelet Transform (DWT), the Wavelet Packet Transform (WPT) and the Stationary WPT (SWPT) are used and compared. The efficiency of the proposed approaches is verified by several experiments corresponding to three types of bearing faults. The wavelet analysis, especially the SWPT, shows its ability to identify the BF signatures more accurately than other wavelet techniques regardless the load level.</description><identifier>EISSN: 2474-0446</identifier><identifier>EISBN: 1538653052</identifier><identifier>EISBN: 9781538653050</identifier><identifier>DOI: 10.1109/SSD.2018.8570403</identifier><language>eng</language><publisher>IEEE</publisher><subject>Bearing fault ; Circuit faults ; Discrete wavelet transforms ; Estimation ; Fault detection ; Induction motor ; Multiple signal classification ; power spectral density ; stator currents ; Wavelet packets ; wavelet transform</subject><ispartof>2018 15th International Multi-Conference on Systems, Signals & Devices (SSD), 2018, p.882-887</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c222t-3c4ba6149201273873d791e75c6f3c2e9ecdcd863fbe24e5f92b6a1ec91e64613</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8570403$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8570403$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ben Abid, Firas</creatorcontrib><creatorcontrib>Braham, Ahmed</creatorcontrib><title>Advanced Signal Processing Techniques for Bearing Fault Detection in Induction Motors</title><title>2018 15th International Multi-Conference on Systems, Signals & Devices (SSD)</title><addtitle>SSD</addtitle><description>This paper focuses on bearing fault (BF) detection in induction motors based on a combination of advanced signal processing tools with motor current signature analysis (MCSA). The applied tools consist on two main techniques: the power spectral density (PSD) estimation for spectral analysis and wavelet techniques for time-frequency analysis. In order to identify the signatures related to bearing faults in the current spectrum, the Welch and the Multiple Signal Classification (MUSIC) PSD estimations are applied. Since the PSD techniques are not appropriate for non-stationary conditions, the Discrete Wavelet Transform (DWT), the Wavelet Packet Transform (WPT) and the Stationary WPT (SWPT) are used and compared. The efficiency of the proposed approaches is verified by several experiments corresponding to three types of bearing faults. The wavelet analysis, especially the SWPT, shows its ability to identify the BF signatures more accurately than other wavelet techniques regardless the load level.</description><subject>Bearing fault</subject><subject>Circuit faults</subject><subject>Discrete wavelet transforms</subject><subject>Estimation</subject><subject>Fault detection</subject><subject>Induction motor</subject><subject>Multiple signal classification</subject><subject>power spectral density</subject><subject>stator currents</subject><subject>Wavelet packets</subject><subject>wavelet transform</subject><issn>2474-0446</issn><isbn>1538653052</isbn><isbn>9781538653050</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2018</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkE1LAzEYhKMgWGvvgpf8ga35_jjW1mqhorDtuWSTd2tkzepmV-i_t9KehhkehmEQuqNkSimxD2W5mDJCzdRITQThF-iGSm6U5ESySzRiQouCCKGu0STnT0IIU4ZrK0ZoOwu_LnkIuIz75Br83rUeco5pjzfgP1L8GSDjuu3wI7juP166oenxAnrwfWwTjgmvUhhO5rXt2y7foqvaNRkmZx2j7fJpM38p1m_Pq_lsXXjGWF9wLyqnqLDH8Uxzo3nQloKWXtXcM7Dggw9G8boCJkDWllXKUfBHSAlF-Rjdn3ojAOy-u_jlusPu_AL_A8hNUMc</recordid><startdate>201803</startdate><enddate>201803</enddate><creator>Ben Abid, Firas</creator><creator>Braham, Ahmed</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201803</creationdate><title>Advanced Signal Processing Techniques for Bearing Fault Detection in Induction Motors</title><author>Ben Abid, Firas ; Braham, Ahmed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c222t-3c4ba6149201273873d791e75c6f3c2e9ecdcd863fbe24e5f92b6a1ec91e64613</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Bearing fault</topic><topic>Circuit faults</topic><topic>Discrete wavelet transforms</topic><topic>Estimation</topic><topic>Fault detection</topic><topic>Induction motor</topic><topic>Multiple signal classification</topic><topic>power spectral density</topic><topic>stator currents</topic><topic>Wavelet packets</topic><topic>wavelet transform</topic><toplevel>online_resources</toplevel><creatorcontrib>Ben Abid, Firas</creatorcontrib><creatorcontrib>Braham, Ahmed</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</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>Ben Abid, Firas</au><au>Braham, Ahmed</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Advanced Signal Processing Techniques for Bearing Fault Detection in Induction Motors</atitle><btitle>2018 15th International Multi-Conference on Systems, Signals & Devices (SSD)</btitle><stitle>SSD</stitle><date>2018-03</date><risdate>2018</risdate><spage>882</spage><epage>887</epage><pages>882-887</pages><eissn>2474-0446</eissn><eisbn>1538653052</eisbn><eisbn>9781538653050</eisbn><abstract>This paper focuses on bearing fault (BF) detection in induction motors based on a combination of advanced signal processing tools with motor current signature analysis (MCSA). The applied tools consist on two main techniques: the power spectral density (PSD) estimation for spectral analysis and wavelet techniques for time-frequency analysis. In order to identify the signatures related to bearing faults in the current spectrum, the Welch and the Multiple Signal Classification (MUSIC) PSD estimations are applied. Since the PSD techniques are not appropriate for non-stationary conditions, the Discrete Wavelet Transform (DWT), the Wavelet Packet Transform (WPT) and the Stationary WPT (SWPT) are used and compared. The efficiency of the proposed approaches is verified by several experiments corresponding to three types of bearing faults. The wavelet analysis, especially the SWPT, shows its ability to identify the BF signatures more accurately than other wavelet techniques regardless the load level.</abstract><pub>IEEE</pub><doi>10.1109/SSD.2018.8570403</doi><tpages>6</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | EISSN: 2474-0446 |
ispartof | 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD), 2018, p.882-887 |
issn | 2474-0446 |
language | eng |
recordid | cdi_ieee_primary_8570403 |
source | IEEE Xplore All Conference Series |
subjects | Bearing fault Circuit faults Discrete wavelet transforms Estimation Fault detection Induction motor Multiple signal classification power spectral density stator currents Wavelet packets wavelet transform |
title | Advanced Signal Processing Techniques for Bearing Fault Detection in Induction Motors |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T14%3A10%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Advanced%20Signal%20Processing%20Techniques%20for%20Bearing%20Fault%20Detection%20in%20Induction%20Motors&rft.btitle=2018%2015th%20International%20Multi-Conference%20on%20Systems,%20Signals%20&%20Devices%20(SSD)&rft.au=Ben%20Abid,%20Firas&rft.date=2018-03&rft.spage=882&rft.epage=887&rft.pages=882-887&rft.eissn=2474-0446&rft_id=info:doi/10.1109/SSD.2018.8570403&rft.eisbn=1538653052&rft.eisbn_list=9781538653050&rft_dat=%3Cieee_CHZPO%3E8570403%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c222t-3c4ba6149201273873d791e75c6f3c2e9ecdcd863fbe24e5f92b6a1ec91e64613%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=8570403&rfr_iscdi=true |