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...

Full description

Saved in:
Bibliographic Details
Main Authors: Ben Abid, Firas, Braham, Ahmed
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 &amp; 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 &amp; 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 &amp; 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