{"id":"https://openalex.org/W4290945393","doi":"https://doi.org/10.1145/3534678.3539057","title":"Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry","display_name":"Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290945393","doi":"https://doi.org/10.1145/3534678.3539057"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539057","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539057","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","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/A5024187448","display_name":"Dovile Juodelyte","orcid":"https://orcid.org/0000-0002-6195-1120"},"institutions":[{"id":"https://openalex.org/I83467386","display_name":"IT University of Copenhagen","ror":"https://ror.org/02309jg23","country_code":"DK","type":"education","lineage":["https://openalex.org/I83467386"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Dovile Juodelyte","raw_affiliation_strings":["IT University of Copenhagen, Copenhagen, Denmark"],"affiliations":[{"raw_affiliation_string":"IT University of Copenhagen, Copenhagen, Denmark","institution_ids":["https://openalex.org/I83467386"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031371103","display_name":"Veronika Cheplygina","orcid":"https://orcid.org/0000-0003-0176-9324"},"institutions":[{"id":"https://openalex.org/I83467386","display_name":"IT University of Copenhagen","ror":"https://ror.org/02309jg23","country_code":"DK","type":"education","lineage":["https://openalex.org/I83467386"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Veronika Cheplygina","raw_affiliation_strings":["IT University of Copenhagen, Copenhagen, Denmark"],"affiliations":[{"raw_affiliation_string":"IT University of Copenhagen, Copenhagen, Denmark","institution_ids":["https://openalex.org/I83467386"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043466681","display_name":"Therese Graversen","orcid":null},"institutions":[{"id":"https://openalex.org/I83467386","display_name":"IT University of Copenhagen","ror":"https://ror.org/02309jg23","country_code":"DK","type":"education","lineage":["https://openalex.org/I83467386"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Therese Graversen","raw_affiliation_strings":["IT University of Copenhagen, Copenhagen, Denmark"],"affiliations":[{"raw_affiliation_string":"IT University of Copenhagen, Copenhagen, Denmark","institution_ids":["https://openalex.org/I83467386"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079584430","display_name":"Philippe Bonnet","orcid":"https://orcid.org/0000-0002-0234-5374"},"institutions":[{"id":"https://openalex.org/I83467386","display_name":"IT University of Copenhagen","ror":"https://ror.org/02309jg23","country_code":"DK","type":"education","lineage":["https://openalex.org/I83467386"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Philippe Bonnet","raw_affiliation_strings":["IT University of Copenhagen, Copenhagen, Denmark"],"affiliations":[{"raw_affiliation_string":"IT University of Copenhagen, Copenhagen, Denmark","institution_ids":["https://openalex.org/I83467386"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5024187448"],"corresponding_institution_ids":["https://openalex.org/I83467386"],"apc_list":null,"apc_paid":null,"fwci":6.7215,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.98402813,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3107","last_page":"3115"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9900000095367432,"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":0.9900000095367432,"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/T13690","display_name":"Quality and Safety in Healthcare","score":0.97079998254776,"subfield":{"id":"https://openalex.org/subfields/3607","display_name":"Medical Laboratory Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13213","display_name":"Mechanical Failure Analysis and Simulation","score":0.9595000147819519,"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/predictive-maintenance","display_name":"Predictive maintenance","score":0.6649275422096252},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6526626944541931},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.59202641248703},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5916752815246582},{"id":"https://openalex.org/keywords/prognostics","display_name":"Prognostics","score":0.556877613067627},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.544213593006134},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4999406337738037},{"id":"https://openalex.org/keywords/vibration","display_name":"Vibration","score":0.4720612168312073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4484069347381592},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.4480382800102234},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4301064610481262},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4222137928009033},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4113119840621948},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3314882218837738},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.3242659270763397},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2571517527103424},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17903956770896912}],"concepts":[{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.6649275422096252},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6526626944541931},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.59202641248703},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5916752815246582},{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.556877613067627},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.544213593006134},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4999406337738037},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.4720612168312073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4484069347381592},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.4480382800102234},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4301064610481262},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4222137928009033},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4113119840621948},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3314882218837738},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3242659270763397},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2571517527103424},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17903956770896912},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3534678.3539057","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539057","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/c9f06d63-b1ee-4883-83b8-ad6ae9d01e06","is_oa":false,"landing_page_url":"https://pure.itu.dk/portal/da/publications/c9f06d63-b1ee-4883-83b8-ad6ae9d01e06","pdf_url":null,"source":{"id":"https://openalex.org/S4377196680","display_name":"IT University Of Copenhagen (IT University of Copenhagen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I83467386","host_organization_name":"IT University of Copenhagen","host_organization_lineage":["https://openalex.org/I83467386"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Juodelyte, D, Cheplygina, V, Graversen, T & Bonnet, P 2022, Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry. i A Zhang & H Rangwala (red), KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. Association for Computing Machinery, s. 3107-3115. https://doi.org/10.1145/3534678.3539057","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1975687401","https://openalex.org/W2017659646","https://openalex.org/W2028472133","https://openalex.org/W2032301033","https://openalex.org/W2037250283","https://openalex.org/W2043281016","https://openalex.org/W2055678181","https://openalex.org/W2079473768","https://openalex.org/W2090668019","https://openalex.org/W2097412031","https://openalex.org/W2113985857","https://openalex.org/W2152074354","https://openalex.org/W2319025975","https://openalex.org/W2342249984","https://openalex.org/W2773549135","https://openalex.org/W2889285628","https://openalex.org/W2904460913","https://openalex.org/W2915337192","https://openalex.org/W2946742526","https://openalex.org/W2997591727","https://openalex.org/W3093022469","https://openalex.org/W3100507333","https://openalex.org/W3116907792"],"related_works":["https://openalex.org/W2028839796","https://openalex.org/W3045935064","https://openalex.org/W2947125308","https://openalex.org/W4321486786","https://openalex.org/W4307290295","https://openalex.org/W4385520395","https://openalex.org/W2586143229","https://openalex.org/W3160450996","https://openalex.org/W2527510741","https://openalex.org/W2908973203"],"abstract_inverted_index":{"In":[0,15,58],"the":[1,4,13,18,45,130],"pharmaceutical":[2],"industry,":[3],"maintenance":[5,22],"of":[6,20,154],"production":[7],"machines":[8],"must":[9],"be":[10],"audited":[11],"by":[12],"regulator.":[14],"this":[16,59],"context,":[17],"problem":[19],"predictive":[21],"is":[23,80,113,141],"not":[24],"when":[25],"to":[26,33,48,115],"maintain":[27,34],"a":[28,36,55,69,81,95,109,117,152],"machine,":[29],"but":[30],"what":[31],"parts":[32,51],"at":[35],"given":[37],"point":[38],"in":[39,94],"time.":[40],"The":[41],"focus":[42,62],"shifts":[43],"from":[44],"entire":[46],"machine":[47],"its":[49],"component":[50],"and":[52,66,143,148],"prediction":[53],"becomes":[54],"classification":[56],"problem.":[57],"paper,":[60],"we":[61,67],"on":[63,88,129],"rolling-elements":[64],"bearings":[65],"propose":[68],"framework":[70,107,140],"for":[71,120,151],"predicting":[72],"their":[73],"degradation":[74,122],"stages":[75],"automatically.":[76],"Our":[77,125],"main":[78],"contribution":[79],"k-means":[82],"bearing":[83,90,121],"lifetime":[84],"segmentation":[85],"method":[86],"based":[87,128],"high-frequency":[89,103],"vibration":[91,104],"signal":[92],"embedded":[93],"latent":[96],"low-dimensional":[97],"subspace":[98],"using":[99],"an":[100],"AutoEncoder.":[101],"Given":[102],"data,":[105],"our":[106,139],"generates":[108],"labeled":[110],"dataset":[111],"that":[112,138,144],"used":[114],"train":[116],"supervised":[118],"model":[119],"stage":[123],"detection.":[124],"experimental":[126],"results,":[127],"publicly":[131],"available":[132],"FEMTO":[133],"Bearing":[134],"run-to-failure":[135],"dataset,":[136],"show":[137],"scalable":[142],"it":[145],"provides":[146],"reliable":[147],"actionable":[149],"predictions":[150],"range":[153],"different":[155],"bearings.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2022-08-13T00:00:00"}
