{"id":"https://openalex.org/W7129376770","doi":"https://doi.org/10.1109/access.2026.3665865","title":"Online Distribution-Based Clustering Algorithm for Condition Monitoring in Rotating Machines","display_name":"Online Distribution-Based Clustering Algorithm for Condition Monitoring in Rotating Machines","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7129376770","doi":"https://doi.org/10.1109/access.2026.3665865"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3665865","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3665865","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3665865","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007757526","display_name":"Alexandre Henrique Pereira Tavares","orcid":"https://orcid.org/0000-0003-3134-0158"},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"A. Tavares","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil"],"raw_orcid":"https://orcid.org/0000-0003-3134-0158","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126228560","display_name":"C. Veiga","orcid":null},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"C. Veiga","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126221115","display_name":"A. Cavalini","orcid":null},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"A. Cavalini","raw_affiliation_strings":["Mechanical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-9647-2621","affiliations":[{"raw_affiliation_string":"Mechanical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126242722","display_name":"C. Nataraj","orcid":null},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"C. Nataraj","raw_affiliation_strings":["Mechanical Engineering Department, Villanova University, Villanova, PA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5816-3932","affiliations":[{"raw_affiliation_string":"Mechanical Engineering Department, Villanova University, Villanova, PA, USA","institution_ids":["https://openalex.org/I7863295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002304085","display_name":"Amirhassan Abbasi","orcid":"https://orcid.org/0000-0002-0953-4928"},"institutions":[{"id":"https://openalex.org/I7863295","display_name":"Villanova University","ror":"https://ror.org/02g7kd627","country_code":"US","type":"education","lineage":["https://openalex.org/I7863295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Abbasi","raw_affiliation_strings":["Mechanical Engineering Department, Villanova University, Villanova, PA, USA"],"raw_orcid":"https://orcid.org/0000-0002-0953-4928","affiliations":[{"raw_affiliation_string":"Mechanical Engineering Department, Villanova University, Villanova, PA, USA","institution_ids":["https://openalex.org/I7863295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126223259","display_name":"V Steffen","orcid":null},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"V. Steffen","raw_affiliation_strings":["Mechanical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil"],"raw_orcid":"https://orcid.org/0000-0001-6124-2554","affiliations":[{"raw_affiliation_string":"Mechanical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126229605","display_name":"M. Cunha","orcid":null},"institutions":[{"id":"https://openalex.org/I80850581","display_name":"Universidade Federal de Uberl\u00e2ndia","ror":"https://ror.org/04x3wvr31","country_code":"BR","type":"education","lineage":["https://openalex.org/I80850581"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"M. Cunha","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-4173-8031","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Uberl&#x00E2;ndia (UFU), Uberl&#x00E2;ndia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.206255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"27565","last_page":"27583"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9894000291824341,"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":0.9894000291824341,"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/T11602","display_name":"Magnetic Bearings and Levitation Dynamics","score":0.00419999985024333,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/cluster-analysis","display_name":"Cluster analysis","score":0.6456000208854675},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6283000111579895},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5769000053405762},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.5645999908447266},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5174000263214111},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4993000030517578},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.47360000014305115},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41110000014305115},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.3977000117301941}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7447999715805054},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6456000208854675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6320000290870667},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6283000111579895},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5769000053405762},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.5645999908447266},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5641000270843506},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5174000263214111},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.47360000014305115},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4207000136375427},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3747999966144562},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.35010001063346863},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.31529998779296875},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3665865","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3665865","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3665865","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3665865","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5740529298782349,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Continuous":[0],"monitoring":[1,64,242],"of":[2,93,141,155,243],"industrial":[3,221],"rotating":[4,135,244],"machines":[5],"is":[6,231],"essential":[7],"for":[8,28,61,240],"ensuring":[9],"health,":[10],"reliability,":[11],"and":[12,97,111,143,152,157,166,187,218,228],"safety,":[13],"while":[14,85,251],"minimizing":[15],"costs":[16],"from":[17,54,95,133,146],"unplanned":[18],"downtime.":[19],"Traditional":[20],"systems":[21],"require":[22],"domain":[23],"experts":[24],"to":[25,49,173,184,203],"configure":[26],"parameters":[27],"each":[29],"machine,":[30],"limiting":[31],"adaptability.":[32],"Machine":[33],"learning":[34,205],"techniques":[35],"offer":[36],"alternatives,":[37],"with":[38,117,125,198],"supervised":[39,204],"approaches":[40],"demanding":[41],"labeled":[42,224],"datasets":[43,145,200],"that":[44],"are":[45,107,226],"rarely":[46],"available":[47],"due":[48],"the":[50,102,138,181,214,234],"extensive":[51],"effort":[52],"required":[53],"specialists.":[55],"This":[56,212],"study":[57],"presents":[58],"an":[59,67,118,237],"algorithm":[60,73,235],"unsupervised":[62,241],"condition":[63,190],"based":[65],"on":[66,101,248],"online":[68],"distribution-driven":[69],"clustering":[70],"approach.":[71],"The":[72],"autonomously":[74],"learns":[75],"machine":[76,136,189],"behavior,":[77],"detecting":[78],"new":[79],"operating":[80],"conditions":[81],"in":[82,192,220],"real":[83,131,193],"time":[84],"recognizing":[86],"previously":[87],"observed":[88],"states.":[89],"Input":[90],"data":[91,225],"consist":[92],"signals":[94,106],"proximity":[96],"accelerometer":[98],"sensors":[99],"mounted":[100],"rotor":[103],"shaft.":[104],"These":[105,178],"processed":[108],"using":[109],"time-":[110],"frequency-domain":[112],"feature":[113],"extraction":[114],"methods":[115],"combined":[116],"autoencoder":[119],"neural":[120],"network.":[121],"Validation":[122],"was":[123],"performed":[124],"four":[126],"datasets:":[127],"synthetically":[128],"generated":[129],"signals,":[130],"measurements":[132],"a":[134],"at":[137],"Federal":[139],"University":[140,150,154],"Uberlandia,":[142],"benchmark":[144,199],"Case":[147],"Western":[148],"Reserve":[149],"(CWRU)":[151],"Hanoi":[153],"Science":[156],"Technology":[158],"(HUST)":[159],"bearing":[160],"dataset.":[161],"Evaluation":[162],"metrics,":[163],"including":[164],"accuracy":[165],"F1":[167],"score,":[168],"consistently":[169],"reached":[170],"values":[171],"close":[172],"0.96":[174],"across":[175],"most":[176],"tests.":[177],"results":[179,196],"confirm":[180],"algorithm\u2019s":[182],"ability":[183],"reliably":[185],"detect":[186],"classify":[188],"variations":[191],"time.":[194],"Furthermore,":[195],"obtained":[197],"were":[201],"compared":[202],"studies,":[206],"showing":[207],"comparable":[208],"or":[209],"superior":[210],"performance.":[211],"demonstrates":[213],"proposed":[215],"method\u2019s":[216],"robustness":[217],"practicality":[219],"applications,":[222],"where":[223],"scarce":[227],"real-time":[229],"adaptability":[230],"critical.":[232],"Overall,":[233],"provides":[236],"effective":[238],"solution":[239],"machinery,":[245],"reducing":[246],"reliance":[247],"expert":[249],"intervention":[250],"maintaining":[252],"high":[253],"detection":[254],"accuracy.":[255]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-18T00:00:00"}
