{"id":"https://openalex.org/W3036640231","doi":"https://doi.org/10.3390/s20123422","title":"Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier","display_name":"Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier","publication_year":2020,"publication_date":"2020-06-17","ids":{"openalex":"https://openalex.org/W3036640231","doi":"https://doi.org/10.3390/s20123422","mag":"3036640231","pmid":"https://pubmed.ncbi.nlm.nih.gov/32560493"},"language":"en","primary_location":{"id":"doi:10.3390/s20123422","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20123422","pdf_url":"https://www.mdpi.com/1424-8220/20/12/3422/pdf?version=1592636429","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/20/12/3422/pdf?version=1592636429","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062828983","display_name":"Mien Van","orcid":"https://orcid.org/0000-0001-9616-6061"},"institutions":[{"id":"https://openalex.org/I126231945","display_name":"Queen's University Belfast","ror":"https://ror.org/00hswnk62","country_code":"GB","type":"education","lineage":["https://openalex.org/I126231945"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mien Van","raw_affiliation_strings":["Centre for Intelligent and Autonomous Manufacturing Systems, and School of Electronics, Electrical Engineering and Computer Science, Queen\u2019s University Belfast, Belfast BT7 1NN, UK","Centre for Intelligent and Autonomous Manufacturing Systems, and School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT7 1NN, UK"],"raw_orcid":"https://orcid.org/0000-0001-9616-6061","affiliations":[{"raw_affiliation_string":"Centre for Intelligent and Autonomous Manufacturing Systems, and School of Electronics, Electrical Engineering and Computer Science, Queen\u2019s University Belfast, Belfast BT7 1NN, UK","institution_ids":["https://openalex.org/I126231945"]},{"raw_affiliation_string":"Centre for Intelligent and Autonomous Manufacturing Systems, and School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT7 1NN, UK","institution_ids":["https://openalex.org/I126231945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089097625","display_name":"Duy-Tang Hoang","orcid":"https://orcid.org/0000-0002-3930-7283"},"institutions":[{"id":"https://openalex.org/I40542001","display_name":"University of Ulsan","ror":"https://ror.org/02c2f8975","country_code":"KR","type":"education","lineage":["https://openalex.org/I40542001"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Duy Tang Hoang","raw_affiliation_strings":["Department of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"],"raw_orcid":"https://orcid.org/0000-0002-3930-7283","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea","institution_ids":["https://openalex.org/I40542001"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107839232","display_name":"Hee\u2010Jun Kang","orcid":"https://orcid.org/0000-0001-9121-5442"},"institutions":[{"id":"https://openalex.org/I40542001","display_name":"University of Ulsan","ror":"https://ror.org/02c2f8975","country_code":"KR","type":"education","lineage":["https://openalex.org/I40542001"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hee Jun Kang","raw_affiliation_strings":["School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"],"raw_orcid":"https://orcid.org/0000-0001-9121-5442","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea","institution_ids":["https://openalex.org/I40542001"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5107839232"],"corresponding_institution_ids":["https://openalex.org/I40542001"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":5.8029,"has_fulltext":true,"cited_by_count":53,"citation_normalized_percentile":{"value":0.96607134,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"20","issue":"12","first_page":"3422","last_page":"3422"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11557","display_name":"Lubricants and Their Additives","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.6902540922164917},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6579281091690063},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6571691632270813},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5999574065208435},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5795379877090454},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5430538058280945},{"id":"https://openalex.org/keywords/relevance-vector-machine","display_name":"Relevance vector machine","score":0.5106359124183655},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.505882203578949},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3687184453010559},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3378065824508667}],"concepts":[{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6902540922164917},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6579281091690063},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6571691632270813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5999574065208435},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5795379877090454},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5430538058280945},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.5106359124183655},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.505882203578949},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3687184453010559},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3378065824508667}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.3390/s20123422","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20123422","pdf_url":"https://www.mdpi.com/1424-8220/20/12/3422/pdf?version=1592636429","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:32560493","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32560493","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pure.qub.ac.uk/portal:publications/d79523d3-318d-47e1-afdf-86542ab8c66c","is_oa":true,"landing_page_url":"https://pure.qub.ac.uk/en/publications/d79523d3-318d-47e1-afdf-86542ab8c66c","pdf_url":"https://pureadmin.qub.ac.uk/ws/files/212569674/revised_manuscript.pdf","source":{"id":"https://openalex.org/S4306402319","display_name":"Research Portal (Queen's University Belfast)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I126231945","host_organization_name":"Queen's University Belfast","host_organization_lineage":["https://openalex.org/I126231945"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Van , M , Hoang , D T & Kang , H J 2020 , ' Bearing Fault Diagnosis using a Particle Swarm Optimization-Least SquaresWavelet Support Vector Machine Classifier ' , Sensors (Basel, Switzerland) . https://doi.org/10.3390/s20123422","raw_type":"article"},{"id":"pmh:oai:doaj.org/article:bf76a8b99a5f41f0b159df0fdebe1cad","is_oa":true,"landing_page_url":"https://doaj.org/article/bf76a8b99a5f41f0b159df0fdebe1cad","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 20, Iss 12, p 3422 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/20/12/3422/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/s20123422","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7349084","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7349084","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s20123422","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20123422","pdf_url":"https://www.mdpi.com/1424-8220/20/12/3422/pdf?version=1592636429","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1197706284","display_name":null,"funder_award_id":"2019R1D1A3A03103528","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320357","display_name":"Queen's University Belfast","ror":"https://ror.org/00hswnk62"},{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321832","display_name":"Queen's University","ror":"https://ror.org/02y72wh86"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3036640231.pdf","grobid_xml":"https://content.openalex.org/works/W3036640231.grobid-xml"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W314653519","https://openalex.org/W1576520375","https://openalex.org/W1596717185","https://openalex.org/W1897892887","https://openalex.org/W1967282395","https://openalex.org/W1996021349","https://openalex.org/W1999902215","https://openalex.org/W2007154098","https://openalex.org/W2007221293","https://openalex.org/W2027872902","https://openalex.org/W2042031583","https://openalex.org/W2086649953","https://openalex.org/W2087273325","https://openalex.org/W2105807304","https://openalex.org/W2119821739","https://openalex.org/W2124776405","https://openalex.org/W2127157362","https://openalex.org/W2151040995","https://openalex.org/W2151419026","https://openalex.org/W2158143121","https://openalex.org/W2164641162","https://openalex.org/W2187071326","https://openalex.org/W2285341434","https://openalex.org/W2293747114","https://openalex.org/W2405580001","https://openalex.org/W2543580944","https://openalex.org/W2562498401","https://openalex.org/W2603944049","https://openalex.org/W2617800229","https://openalex.org/W2793692345","https://openalex.org/W2796986051","https://openalex.org/W2894598866","https://openalex.org/W2908593881","https://openalex.org/W2922497381","https://openalex.org/W2936033955","https://openalex.org/W2965625921","https://openalex.org/W2980627310","https://openalex.org/W2993435965","https://openalex.org/W3016807964","https://openalex.org/W3030453392","https://openalex.org/W3101025102","https://openalex.org/W4239510810","https://openalex.org/W6682642761","https://openalex.org/W6769250439","https://openalex.org/W6771262152"],"related_works":["https://openalex.org/W2010005619","https://openalex.org/W2066952721","https://openalex.org/W2565312173","https://openalex.org/W2683383161","https://openalex.org/W2335152656","https://openalex.org/W3147035969","https://openalex.org/W2359936972","https://openalex.org/W2383027800","https://openalex.org/W2206547991","https://openalex.org/W2104936869"],"abstract_inverted_index":{"Bearing":[0],"is":[1,18,39,72,92,120,140,160],"one":[2],"of":[3,7,15,19,81,84,126,145,157,169,180,187],"the":[4,16,96,99,105,143,167,177,188],"key":[5,154],"components":[6],"a":[8,25,43,46,48,53,75,88,115,124,135,146,163,195,200],"rotating":[9],"machine.":[10],"Hence,":[11],"monitoring":[12],"health":[13],"condition":[14],"bearing":[17,63,69,181,197],"paramount":[20],"importace.":[21],"This":[22],"paper":[23],"develops":[24],"novel":[26,136],"particle":[27],"swarm":[28],"optimization":[29],"(PSO)-least":[30],"squares":[31,50],"wavelet":[32,55,172],"support":[33,58],"vector":[34,59],"machine":[35,60],"(PSO-LSWSVM)":[36],"classifier,":[37,137],"which":[38,79,101],"designed":[40],"based":[41,193],"on":[42,194],"combination":[44],"between":[45],"PSO,":[47,147],"least":[49],"procedure,":[51],"and":[52,109,199],"new":[54,164,171],"kernel":[56,173],"function-based":[57],"(SVM),":[61],"for":[62],"fault":[64,70,182],"diagnosis.":[65,183],"In":[66],"this":[67,158],"work,":[68],"classification":[71,151,178],"transformed":[73],"into":[74],"pattern":[76],"recognition":[77],"problem,":[78],"consists":[80],"three":[82],"stages":[83],"data":[85],"processing.":[86],"Firstly,":[87],"rich":[89],"information":[90],"dataset":[91,198],"built":[93],"by":[94,104],"extracting":[95],"features":[97,186],"from":[98],"signals,":[100],"are":[102,191],"decomposed":[103],"nonlocal":[106],"means":[107],"(NLM)":[108],"empirical":[110],"mode":[111],"decomposition":[112],"(EMD).":[113],"Secondly,":[114],"minimum-redundancy":[116],"maximum-relevance":[117],"(mRMR)":[118],"method":[119],"employed":[121],"to":[122,148,161,175],"determine":[123],"subset":[125],"feature":[127],"that":[128],"can":[129],"provide":[130,149],"an":[131,170],"optimal":[132],"performance.":[133],"Thirdly,":[134],"namely":[138],"LSWSVM,":[139],"proposed":[141,189],"with":[142,166],"aid":[144,168],"higher":[150],"accuracy.":[152],"The":[153,184],"innovative":[155],"science":[156],"work":[159],"propropose":[162],"classifier":[165],"type":[174],"increase":[176],"precision":[179],"merit":[185],"approach":[190],"demonstrated":[192],"benchmark":[196],"comprehensive":[201],"comparison":[202],"procedure.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":4}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}