{"id":"https://openalex.org/W4414538307","doi":"https://doi.org/10.1109/access.2025.3615116","title":"A One-Shot Learning Approach for Fault Classification of Bearings via Multi-Autoencoder Reconstruction","display_name":"A One-Shot Learning Approach for Fault Classification of Bearings via Multi-Autoencoder Reconstruction","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4414538307","doi":"https://doi.org/10.1109/access.2025.3615116"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3615116","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3615116","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.2025.3615116","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062357152","display_name":"Eduardo Borges Gouveia","orcid":"https://orcid.org/0000-0003-2135-3844"},"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":"Eduardo B. Gouveia","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Uberlândia, MG, Brazil"],"raw_orcid":"https://orcid.org/0000-0003-2135-3844","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Uberlândia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001366815","display_name":"Amanda Jorge","orcid":"https://orcid.org/0000-0001-9319-3371"},"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":"Amanda R. F. Jorge","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Uberlândia, MG, Brazil"],"raw_orcid":"https://orcid.org/0000-0001-9319-3371","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Uberlândia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066406055","display_name":"Aldemir Ap Cavalini","orcid":"https://orcid.org/0000-0002-9647-2621"},"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":"Aldemir Ap. Cavallini","raw_affiliation_strings":["LMEst–Laboratory of Mechanics and Structures, Federal University of Uberlândia, MG, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LMEst–Laboratory of Mechanics and Structures, Federal University of Uberlândia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046533592","display_name":"M\u00e1rcio Jos\u00e9 da Cunha","orcid":"https://orcid.org/0000-0002-4173-8031"},"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\u00e1rcio J. Cunha","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Uberlândia, MG, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-4173-8031","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Uberlândia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067271104","display_name":"L.C. de Freitas","orcid":"https://orcid.org/0000-0002-1036-2801"},"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":"Luiz C. G. De Freitas","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Uberlândia, MG, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-1036-2801","affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Uberlândia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032607279","display_name":"Marcus Ant\u00f4nio Viana Duarte","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":"Marcus A. V. Duarte","raw_affiliation_strings":["LAV-Laboratory of Acoustics and Vibration, Federal University of Uberlândia, MG, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LAV-Laboratory of Acoustics and Vibration, Federal University of Uberlândia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078083909","display_name":"Valder Steffen","orcid":"https://orcid.org/0000-0001-6124-2554"},"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":"Valder Steffen","raw_affiliation_strings":["LMEst–Laboratory of Mechanics and Structures, Federal University of Uberlândia, MG, Brazil"],"raw_orcid":"https://orcid.org/0000-0001-6124-2554","affiliations":[{"raw_affiliation_string":"LMEst–Laboratory of Mechanics and Structures, Federal University of Uberlândia, MG, Brazil","institution_ids":["https://openalex.org/I80850581"]}]}],"institutions":[],"countries_distinct_count":1,"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":0.28939716,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9908000230789185,"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":0.9908000230789185,"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":0.9832000136375427,"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/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9417999982833862,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/autoencoder","display_name":"Autoencoder","score":0.7750999927520752},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.6518999934196472},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6032000184059143},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5558000206947327},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5044999718666077},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.48539999127388},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"Signal reconstruction","score":0.439300000667572},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4325999915599823}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7750999927520752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6593999862670898},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.6518999934196472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6040999889373779},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6032000184059143},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5558000206947327},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5044999718666077},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.48539999127388},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.439300000667572},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42879998683929443},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4162999987602234},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4122999906539917},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3831999897956848},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.37290000915527344},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2754000127315521}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3615116","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3615116","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"},{"id":"pmh:oai:doaj.org/article:7197b0734b5545a089c64144944fa97b","is_oa":true,"landing_page_url":"https://doaj.org/article/7197b0734b5545a089c64144944fa97b","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":"IEEE Access, Vol 13, Pp 170660-170669 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3615116","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3615116","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":[],"awards":[{"id":"https://openalex.org/G2477306535","display_name":null,"funder_award_id":"PD-00642-0124/2024-ANEEL","funder_id":"https://openalex.org/F4320324178","funder_display_name":"Ag\u00eancia Nacional de Energia El\u00e9trica"},{"id":"https://openalex.org/G4385024714","display_name":null,"funder_award_id":"06881/2022-7","funder_id":"https://openalex.org/F4320321091","funder_display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior"},{"id":"https://openalex.org/G5801759199","display_name":null,"funder_award_id":"06148/2022-8","funder_id":"https://openalex.org/F4320321091","funder_display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior"},{"id":"https://openalex.org/G646458537","display_name":null,"funder_award_id":"APQ-04929-22","funder_id":"https://openalex.org/F4320322980","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais"},{"id":"https://openalex.org/G7800132068","display_name":null,"funder_award_id":"APQ-04034-24","funder_id":"https://openalex.org/F4320322980","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais"}],"funders":[{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320322980","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de Minas Gerais","ror":"https://ror.org/00nc55f03"},{"id":"https://openalex.org/F4320324178","display_name":"Ag\u00eancia Nacional de Energia El\u00e9trica","ror":"https://ror.org/049sgh675"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,120],"industrial":[1,222],"scenarios,":[2,175],"the":[3,19,46,60,70,81,88,94,171],"scarcity":[4],"of":[5,11,21,50,166],"labeled":[6],"data":[7],"for":[8,216],"fault":[9,31,52,84,110,118,217],"diagnosis":[10,32,218],"rotating":[12],"machinery":[13],"poses":[14],"a":[15,39,207],"significant":[16],"challenge":[17],"to":[18,44,79,212],"development":[20],"reliable":[22],"data-driven":[23],"models.":[24],"This":[25],"paper":[26],"proposes":[27],"an":[28],"interpretable":[29],"one-shot":[30],"approach":[33],"based":[34],"on":[35,93],"class-specific":[36,204],"autoencoder,":[37],"where":[38],"separate":[40],"autoencoder":[41,205],"is":[42,77],"trained":[43,125],"reconstruct":[45],"vibration":[47],"signal":[48,201],"pattern":[49],"each":[51,144],"class.":[53,85],"The":[54,146],"method":[55],"operates":[56],"effectively":[57],"without":[58],"requiring":[59],"extensive":[61],"meta-training":[62],"phases":[63],"required":[64],"by":[65,126],"few-shot":[66,181],"learning.":[67],"During":[68],"inference,":[69],"reconstruction":[71,202],"similarity":[72],"measured":[73],"via":[74],"Pearson":[75],"correlation":[76],"used":[78],"identify":[80],"most":[82],"likely":[83],"To":[86],"evaluate":[87],"method,":[89],"experiments":[90],"were":[91,124],"conducted":[92],"Case":[95],"Western":[96],"Reserve":[97],"University":[98],"(CWRU)":[99],"dataset":[100],"under":[101],"two":[102],"classification":[103],"schemes:":[104],"one":[105,129],"with":[106,114,178,203],"4":[107],"classes":[108,116],"representing":[109],"types,":[111],"and":[112,133,160,168,173,180,195],"another":[113],"10":[115],"including":[117,184],"severities.":[119],"both":[121],"cases,":[122],"models":[123],"using":[127,140],"only":[128],"sample":[130],"per":[131],"class,":[132],"performance":[134,151],"was":[135],"averaged":[136],"over":[137],"50-fold":[138],"cross-validation":[139],"different":[141],"samples":[142],"in":[143,170,219],"fold.":[145],"proposed":[147],"model":[148],"achieved":[149],"superior":[150],"across":[152],"all":[153],"tested":[154],"operating":[155],"conditions":[156],"(1797,":[157],"1772,":[158],"1750,":[159],"1730":[161],"RPM),":[162],"reaching":[163],"average":[164],"accuracies":[165],"64%":[167],"67%":[169],"10-class":[172],"4-class":[174],"respectively.":[176],"Comparisons":[177],"conventional":[179],"learning":[182],"methods":[183],"Long":[185],"Short-Term":[186],"Memory":[187],"(LSTM),":[188],"Residual":[189],"Network":[190],"(ResNet18-1D),":[191],"Model-Agnostic":[192],"Meta-Learning":[193],"(MAML),":[194],"Prototypical":[196],"Networks.":[197],"These":[198],"results":[199],"establish":[200],"as":[206],"simple":[208],"yet":[209],"powerful":[210],"alternative":[211],"complex":[213],"meta-learning":[214],"strategies":[215],"real-world,":[220],"data-scarce,":[221],"environments.":[223]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}