{"id":"https://openalex.org/W2959391348","doi":"https://doi.org/10.1109/icphm.2019.8819383","title":"Unsupervised Fault Detection in Varying Operating Conditions","display_name":"Unsupervised Fault Detection in Varying Operating Conditions","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2959391348","doi":"https://doi.org/10.1109/icphm.2019.8819383","mag":"2959391348"},"language":"en","primary_location":{"id":"doi:10.1109/icphm.2019.8819383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm.2019.8819383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1907.06481","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060018856","display_name":"Gabriel Michau","orcid":"https://orcid.org/0000-0001-6882-2906"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":true,"raw_author_name":"Gabriel Michau","raw_affiliation_strings":["ETH Zurich, Zurich, Switzerland","ETH Z\u00fcrich"],"affiliations":[{"raw_affiliation_string":"ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]},{"raw_affiliation_string":"ETH Z\u00fcrich","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079637160","display_name":"Olga Fink","orcid":"https://orcid.org/0000-0002-9546-1488"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Olga Fink","raw_affiliation_strings":["ETH Zurich, Zurich, Switzerland","ETH Z\u00fcrich"],"affiliations":[{"raw_affiliation_string":"ETH Zurich, Zurich, Switzerland","institution_ids":["https://openalex.org/I35440088"]},{"raw_affiliation_string":"ETH Z\u00fcrich","institution_ids":["https://openalex.org/I35440088"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5060018856"],"corresponding_institution_ids":["https://openalex.org/I35440088"],"apc_list":null,"apc_paid":null,"fwci":0.3361,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.58146663,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9987000226974487,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9987000226974487,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9937000274658203,"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":0.9787999987602234,"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/computer-science","display_name":"Computer science","score":0.664291501045227},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.6348676681518555},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6254401206970215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6048225164413452},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5594378709793091},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5520737171173096},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.5460413694381714},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5443153381347656},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5054061412811279},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.474896103143692},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4688984453678131},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4481474757194519},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4377695322036743},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4291137158870697},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4129670560359955},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39332517981529236}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.664291501045227},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6348676681518555},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6254401206970215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6048225164413452},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5594378709793091},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5520737171173096},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.5460413694381714},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5443153381347656},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5054061412811279},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.474896103143692},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4688984453678131},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4481474757194519},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4377695322036743},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4291137158870697},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4129670560359955},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39332517981529236},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","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}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icphm.2019.8819383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm.2019.8819383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1907.06481","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.06481","pdf_url":"https://arxiv.org/pdf/1907.06481","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1907.06481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1907.06481","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"},{"id":"mag:2959391348","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1907.06481","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1907.06481","pdf_url":"https://arxiv.org/pdf/1907.06481","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1673543164","https://openalex.org/W1731081199","https://openalex.org/W1982696459","https://openalex.org/W2054570131","https://openalex.org/W2100556411","https://openalex.org/W2141695047","https://openalex.org/W2161336914","https://openalex.org/W2164943005","https://openalex.org/W2495649841","https://openalex.org/W2515979703","https://openalex.org/W2556013418","https://openalex.org/W2563794395","https://openalex.org/W2575190391","https://openalex.org/W2588900524","https://openalex.org/W2607512710","https://openalex.org/W2731372149","https://openalex.org/W2789630537","https://openalex.org/W2891319189","https://openalex.org/W2892596731","https://openalex.org/W2908174393","https://openalex.org/W2963870446","https://openalex.org/W2973319128","https://openalex.org/W3006830200","https://openalex.org/W3161231236","https://openalex.org/W6637618735","https://openalex.org/W6734507031","https://openalex.org/W6767177212","https://openalex.org/W6767738312","https://openalex.org/W6909837239","https://openalex.org/W6947497262","https://openalex.org/W6981771720"],"related_works":["https://openalex.org/W2971202925","https://openalex.org/W2999122368","https://openalex.org/W3109541357","https://openalex.org/W2740497745","https://openalex.org/W3063636603","https://openalex.org/W1907114107","https://openalex.org/W2914786494","https://openalex.org/W2998567984","https://openalex.org/W3044376627","https://openalex.org/W3128659341","https://openalex.org/W2189214667","https://openalex.org/W3004854517","https://openalex.org/W3108835755","https://openalex.org/W2980172860","https://openalex.org/W2891092692","https://openalex.org/W2786492859","https://openalex.org/W2608431727","https://openalex.org/W2592000475","https://openalex.org/W3192668417","https://openalex.org/W1973782578"],"abstract_inverted_index":{"Training":[0],"data-driven":[1],"approaches":[2,74,83,121,202,218],"for":[3,38],"complex":[4],"industrial":[5],"system":[6,49,64],"health":[7],"monitoring":[8],"is":[9,40,97,256],"challenging.":[10],"When":[11],"data":[12,56,87,125,148,232],"on":[13,85,99,205],"faulty":[14],"conditions":[15,115],"are":[16,137,152,189,203,221,239],"rare":[17],"or":[18],"not":[19,58],"available,":[20],"the":[21,34,48,54,63,86,89,95,100,104,147,155,161,192,196,225,237,245,253],"training":[22,55,156,193],"has":[23],"to":[24,43,46,75,91,111,140,168,224,241],"be":[25,44,59,92,242],"performed":[26],"in":[27,50,79,154,160,191,236,252],"a":[28,131,164,171,206],"unsupervised":[29],"manner.":[30],"In":[31,68,133],"addition,":[32],"when":[33],"observation":[35],"period,":[36],"used":[37],"training,":[39],"kept":[41],"short,":[42],"able":[45],"monitor":[47],"its":[51],"early":[52,101],"life,":[53],"might":[57],"representative":[60],"of":[61,103,124,174,186,195,215,231],"all":[62],"normal":[65],"operating":[66,114],"conditions.":[67],"this":[69],"paper,":[70],"we":[71],"propose":[72],"five":[73],"perform":[76],"fault":[77,197],"detection":[78,198],"such":[80],"context.":[81],"Two":[82],"rely":[84],"from":[88,126,149],"unit":[90],"monitored":[93],"only:":[94],"baseline":[96],"trained":[98,227],"life":[102],"unit.":[105],"An":[106],"incremental":[107],"learning":[108],"procedure":[109],"tries":[110],"learn":[112],"new":[113,165,246],"as":[116],"they":[117],"arise.":[118],"Three":[119],"other":[120,127,142],"take":[122],"advantage":[123],"similar":[128,150],"units":[129,136,151,176,188,235,251],"within":[130],"fleet.":[132],"two":[134,229],"cases,":[135],"directly":[138],"compared":[139],"each":[141],"with":[143,177,228],"similarity":[144],"measures,":[145],"and":[146],"combined":[153,190],"set.":[157],"We":[158],"propose,":[159],"third":[162],"case,":[163],"deep-learning":[166],"methodology":[167],"perform,":[169],"first,":[170],"feature":[172,254],"alignment":[173],"different":[175],"an":[178,222],"Unsupervised":[179],"Feature":[180],"Alignment":[181],"Network":[182],"(UFAN).":[183],"Then,":[184],"features":[185],"both":[187],"set":[194],"neural":[199],"network.":[200],"The":[201],"tested":[204],"fleet":[207,238],"comprising":[208],"112":[209],"units,":[210],"observed":[211],"over":[212],"one":[213],"year":[214],"data.":[216],"All":[217],"proposed":[219],"here":[220],"improvement":[223],"baseline,":[226],"months":[230],"only.":[233],"As":[234],"found":[240],"very":[243],"dissimilar,":[244],"architecture":[247],"UFAN,":[248],"that":[249],"aligns":[250],"space,":[255],"outperforming":[257],"others.":[258]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
