No Cover Image

Journal article 420 views 282 downloads

Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM

Narges Khadem Hosseini, Hamid Toshani, Salman Abdi Orcid Logo, Sara Sharifzadeh Orcid Logo

IEEE Transactions on Industry Applications, Volume: 61, Issue: 3, Pages: 3623 - 3636

Swansea University Author: Sara Sharifzadeh Orcid Logo

  • 68949.AAM.pdf

    PDF | Accepted Manuscript

    Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy.

    Download (1.58MB)

Check full text

DOI (Published version): 10.1109/tia.2025.3536425

Published in: IEEE Transactions on Industry Applications
ISSN: 0093-9994 1939-9367
Published: Institute of Electrical and Electronics Engineers (IEEE) 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa68949
first_indexed 2025-02-23T22:43:12Z
last_indexed 2025-07-09T05:00:49Z
id cronfa68949
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-07-08T12:33:01.9457773</datestamp><bib-version>v2</bib-version><id>68949</id><entry>2025-02-23</entry><title>Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM</title><swanseaauthors><author><sid>a4e15f304398ecee3f28c7faec69c1b0</sid><ORCID>0000-0003-4621-2917</ORCID><firstname>Sara</firstname><surname>Sharifzadeh</surname><name>Sara Sharifzadeh</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-02-23</date><deptcode>MACS</deptcode><abstract/><type>Journal Article</type><journal>IEEE Transactions on Industry Applications</journal><volume>61</volume><journalNumber>3</journalNumber><paginationStart>3623</paginationStart><paginationEnd>3636</paginationEnd><publisher>Institute of Electrical and Electronics Engineers (IEEE)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0093-9994</issnPrint><issnElectronic>1939-9367</issnElectronic><keywords>Support vector machines, Recurrent neural networks, Pipeline processing, Fault detection, Feature extraction, Accuracy, Induction motors, Fault diagnosis, Optimization, Current measurement</keywords><publishedDay>1</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-05-01</publishedDate><doi>10.1109/tia.2025.3536425</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm>Not Required</apcterm><funders/><projectreference/><lastEdited>2025-07-08T12:33:01.9457773</lastEdited><Created>2025-02-23T22:26:52.8031225</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Narges Khadem</firstname><surname>Hosseini</surname><order>1</order></author><author><firstname>Hamid</firstname><surname>Toshani</surname><order>2</order></author><author><firstname>Salman</firstname><surname>Abdi</surname><orcid>0000-0002-5424-4479</orcid><order>3</order></author><author><firstname>Sara</firstname><surname>Sharifzadeh</surname><orcid>0000-0003-4621-2917</orcid><order>4</order></author></authors><documents><document><filename>68949__33870__e9f5e0a3a29e4192ab119ab6c8d8c951.pdf</filename><originalFilename>68949.AAM.pdf</originalFilename><uploaded>2025-03-25T09:06:00.1353658</uploaded><type>Output</type><contentLength>1657931</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><documentNotes>Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-07-08T12:33:01.9457773 v2 68949 2025-02-23 Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2025-02-23 MACS Journal Article IEEE Transactions on Industry Applications 61 3 3623 3636 Institute of Electrical and Electronics Engineers (IEEE) 0093-9994 1939-9367 Support vector machines, Recurrent neural networks, Pipeline processing, Fault detection, Feature extraction, Accuracy, Induction motors, Fault diagnosis, Optimization, Current measurement 1 5 2025 2025-05-01 10.1109/tia.2025.3536425 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required 2025-07-08T12:33:01.9457773 2025-02-23T22:26:52.8031225 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Narges Khadem Hosseini 1 Hamid Toshani 2 Salman Abdi 0000-0002-5424-4479 3 Sara Sharifzadeh 0000-0003-4621-2917 4 68949__33870__e9f5e0a3a29e4192ab119ab6c8d8c951.pdf 68949.AAM.pdf 2025-03-25T09:06:00.1353658 Output 1657931 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy. true eng https://creativecommons.org/licenses/by/4.0/
title Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM
spellingShingle Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM
Sara Sharifzadeh
title_short Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM
title_full Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM
title_fullStr Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM
title_full_unstemmed Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM
title_sort Enhanced Bearing Fault Detection in Induction Motors Using Projection-Based SVM
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Narges Khadem Hosseini
Hamid Toshani
Salman Abdi
Sara Sharifzadeh
format Journal article
container_title IEEE Transactions on Industry Applications
container_volume 61
container_issue 3
container_start_page 3623
publishDate 2025
institution Swansea University
issn 0093-9994
1939-9367
doi_str_mv 10.1109/tia.2025.3536425
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
published_date 2025-05-01T05:25:40Z
_version_ 1851369497833766912
score 11.089572

Similar Items