{"id":"https://openalex.org/W3212943159","doi":"https://doi.org/10.1109/iecon48115.2021.9589342","title":"A Proposal to Mitigate Similarity Bias for the Paderborn Bearing Data Set","display_name":"A Proposal to Mitigate Similarity Bias for the Paderborn Bearing Data Set","publication_year":2021,"publication_date":"2021-10-13","ids":{"openalex":"https://openalex.org/W3212943159","doi":"https://doi.org/10.1109/iecon48115.2021.9589342","mag":"3212943159"},"language":"en","primary_location":{"id":"doi:10.1109/iecon48115.2021.9589342","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon48115.2021.9589342","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2021 \u2013 47th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005232745","display_name":"Lucio Antonio Stange Venturim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210160371","display_name":"Instituto Federal do Esp\u00edrito Santo","ror":"https://ror.org/05rshs160","country_code":"BR","type":"funder","lineage":["https://openalex.org/I4210160371"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Lucio Antonio Stange Venturim","raw_affiliation_strings":["Programa de P\u00f3s-gradua\u00e7\u00e3o ~ em Computa\u00e7ao Aplicada (PPComp), Campus Serra do Instituto Federal do Esp\u00edrito Santo (IFES), Serra, ES, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Programa de P\u00f3s-gradua\u00e7\u00e3o ~ em Computa\u00e7ao Aplicada (PPComp), Campus Serra do Instituto Federal do Esp\u00edrito Santo (IFES), Serra, ES, Brazil","institution_ids":["https://openalex.org/I4210160371"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078430550","display_name":"Francisco de Assis Boldt","orcid":"https://orcid.org/0000-0001-6919-5377"},"institutions":[{"id":"https://openalex.org/I4210160371","display_name":"Instituto Federal do Esp\u00edrito Santo","ror":"https://ror.org/05rshs160","country_code":"BR","type":"funder","lineage":["https://openalex.org/I4210160371"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Francisco de Assis Boldt","raw_affiliation_strings":["Programa de P\u00f3s-gradua\u00e7\u00e3o ~ em Computa\u00e7ao Aplicada (PPComp), Campus Serra do Instituto Federal do Esp\u00edrito Santo (IFES), Serra, ES, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Programa de P\u00f3s-gradua\u00e7\u00e3o ~ em Computa\u00e7ao Aplicada (PPComp), Campus Serra do Instituto Federal do Esp\u00edrito Santo (IFES), Serra, ES, Brazil","institution_ids":["https://openalex.org/I4210160371"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210160371"],"apc_list":null,"apc_paid":null,"fwci":0.9,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.68977964,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9811999797821045,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9729999899864197,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.8696876764297485},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6418159604072571},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.592955470085144},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5790282487869263},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.5698851346969604},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5449773669242859},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5254026055335999},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4993174076080322},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.46036139130592346},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4487728178501129},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4270945191383362},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.41318440437316895},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.395169734954834}],"concepts":[{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.8696876764297485},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6418159604072571},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.592955470085144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5790282487869263},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.5698851346969604},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5449773669242859},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5254026055335999},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4993174076080322},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.46036139130592346},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4487728178501129},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4270945191383362},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.41318440437316895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.395169734954834},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iecon48115.2021.9589342","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iecon48115.2021.9589342","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IECON 2021 \u2013 47th Annual Conference of the IEEE Industrial Electronics Society","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.49000000953674316,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W252122814","https://openalex.org/W1510598017","https://openalex.org/W1969989373","https://openalex.org/W2122111042","https://openalex.org/W2166042538","https://openalex.org/W2181304182","https://openalex.org/W2387660017","https://openalex.org/W2488793338","https://openalex.org/W2530133016","https://openalex.org/W2793629656","https://openalex.org/W2893747136","https://openalex.org/W2911964244","https://openalex.org/W2916091221","https://openalex.org/W2989695365","https://openalex.org/W3009370740","https://openalex.org/W3089906026","https://openalex.org/W3090887241","https://openalex.org/W3127919876","https://openalex.org/W3181414820","https://openalex.org/W4235594362","https://openalex.org/W4249625715","https://openalex.org/W4285790115","https://openalex.org/W6630524603","https://openalex.org/W6710909407","https://openalex.org/W6728255800","https://openalex.org/W6767327189","https://openalex.org/W6784100612","https://openalex.org/W6831462957"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W2786391746","https://openalex.org/W3132346564","https://openalex.org/W2991483587","https://openalex.org/W2914559142","https://openalex.org/W4381430104","https://openalex.org/W4226059458","https://openalex.org/W2995102745","https://openalex.org/W1990237101"],"abstract_inverted_index":{"Similarity":[0],"bias":[1,67,108],"is":[2,133],"a":[3,11,61],"phenomenon":[4,28],"that":[5,105,129],"might":[6],"occur":[7],"when":[8,46],"samples":[9],"in":[10],"data":[12,85,100],"set":[13,101],"are":[14,21],"originated":[15],"from":[16,23],"the":[17,47,65,74,83,98,106,130,137],"same":[18],"acquisition":[19],"or":[20],"acquired":[22],"similar":[24],"equipment":[25],"settings.":[26],"This":[27,58],"may":[29],"lead":[30],"to":[31,63,135],"overoptimistic":[32,39],"evaluations":[33],"of":[34,94],"machine":[35],"learning":[36],"algorithms.":[37],"That":[38],"estimation":[40],"probably":[41],"will":[42,53],"not":[43,110],"be":[44,54],"reflected":[45],"models":[48,96],"generated":[49],"by":[50],"these":[51],"algorithms":[52],"put":[55],"on":[56,81],"production.":[57],"paper":[59],"proposes":[60],"method":[62,79,132],"mitigate":[64,136],"similarity":[66,107,138],"for":[68,87,97],"bearing":[69,76],"fault":[70],"diagnosis":[71],"evaluation":[72],"with":[73,115],"Paderborn":[75,99],"dataset.":[77],"The":[78],"consists":[80],"defining":[82],"proper":[84],"splits":[86],"training,":[88],"validation":[89],"and":[90,120,125],"testing.":[91],"Related":[92],"works":[93],"classification":[95,122],"were":[102,113],"evaluated,":[103],"showing":[104],"was":[109],"considered.":[111],"Experiments":[112],"performed":[114],"K-nearest":[116],"neighbors,":[117],"Random":[118],"Forest":[119],"FaultNet":[121],"models.":[123],"Accuracy":[124],"F1-score":[126],"results":[127],"show":[128],"proposed":[131],"effective":[134],"bias.":[139]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}