{"id":"https://openalex.org/W4412030959","doi":"https://doi.org/10.1109/icphm65385.2025.11062032","title":"Semi-supervised domain adaptation with auxiliary task learning for RUL prediction","display_name":"Semi-supervised domain adaptation with auxiliary task learning for RUL prediction","publication_year":2025,"publication_date":"2025-06-09","ids":{"openalex":"https://openalex.org/W4412030959","doi":"https://doi.org/10.1109/icphm65385.2025.11062032"},"language":"en","primary_location":{"id":"doi:10.1109/icphm65385.2025.11062032","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm65385.2025.11062032","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Prognostics and Health Management (ICPHM)","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/A5045459109","display_name":"Gengyu Li","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Gengyu Li","raw_affiliation_strings":["The University of Tokyo,Department of Advanced Interdisciplinary Studies,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Department of Advanced Interdisciplinary Studies,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105470668","display_name":"Takehisa Yairi","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takehisa Yairi","raw_affiliation_strings":["The University of Tokyo,Research Center for Advanced Science and Technology,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Research Center for Advanced Science and Technology,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5045459109"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08729232,"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8930000066757202,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.8930000066757202,"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"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.8574000000953674,"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"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.824400007724762,"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/domain-adaptation","display_name":"Domain adaptation","score":0.8584964275360107},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7191492915153503},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.6700171232223511},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6539075374603271},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5742402076721191},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5346725583076477},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.495364785194397},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12751543521881104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11568227410316467},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10484611988067627}],"concepts":[{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.8584964275360107},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7191492915153503},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6700171232223511},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6539075374603271},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5742402076721191},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5346725583076477},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.495364785194397},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12751543521881104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11568227410316467},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10484611988067627},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icphm65385.2025.11062032","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm65385.2025.11062032","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Prognostics and Health Management (ICPHM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2120841219","https://openalex.org/W2489692225","https://openalex.org/W2591055632","https://openalex.org/W2744067593","https://openalex.org/W2772084711","https://openalex.org/W2903936867","https://openalex.org/W2975761873","https://openalex.org/W3085046840","https://openalex.org/W3119743098","https://openalex.org/W4292976134","https://openalex.org/W4320480791","https://openalex.org/W4387978343","https://openalex.org/W4399916558"],"related_works":["https://openalex.org/W2997567050","https://openalex.org/W4394775207","https://openalex.org/W4389474468","https://openalex.org/W4300172004","https://openalex.org/W3203792196","https://openalex.org/W4321649381","https://openalex.org/W2997645659","https://openalex.org/W3180787869","https://openalex.org/W4295929828","https://openalex.org/W3156096827"],"abstract_inverted_index":{"Recent":[0],"remaining":[1],"useful":[2],"life":[3],"(RUL)":[4],"prediction":[5,139,214],"mainly":[6],"involves":[7],"data-driven":[8,36],"approaches.":[9],"However,":[10],"due":[11],"to":[12,50,86,121,136],"different":[13,147],"operation":[14],"conditions":[15],"and":[16,23,63,93,128,153,185,215],"degradation":[17,127,148],"modes,":[18],"the":[19,123,131,137,151,161,175,179,182,189,210,213,217],"distributions":[20],"of":[21,35,126,181,188,212,220],"training":[22],"test":[24,33],"data":[25,144],"can":[26,207],"be":[27],"quite":[28],"different,":[29],"resulting":[30],"in":[31,61,224],"poor":[32],"performance":[34],"methods,":[37],"known":[38],"as":[39,150,167],"a":[40,88,95,103],"domain":[41,71,76,91,163,170,184,192],"shift":[42],"problem.":[43],"Domain":[44],"adaptation":[45,72,77],"(DA)":[46],"has":[47,79],"been":[48,81],"applied":[49],"address":[51],"this":[52,134],"problem":[53,98],"by":[54],"extracting":[55],"domain-invariant":[56],"features.":[57],"Current":[58],"DA":[59],"applications":[60],"prognostics":[62],"health":[64],"management":[65],"(PHM)":[66],"research":[67],"focus":[68],"on":[69],"unsupervised":[70],"(UDA)":[73],"while":[74],"semi-supervised":[75],"(SSDA)":[78],"not":[80],"widely":[82],"studied.":[83],"SSDA":[84,105,159,205],"allows":[85],"access":[87],"few":[89],"target":[90,154,169,191],"labels":[92,164,180],"is":[94,196],"more":[96],"practical":[97],"setting.":[99],"This":[100],"work":[101],"proposes":[102],"novel":[104],"approach":[106,206],"with":[107,146],"auxiliary":[108,118,221],"task":[109,120,135,222],"learning":[110,223],"for":[111,133,198],"RUL":[112,138],"prediction.":[113],"We":[114],"first":[115],"design":[116],"an":[117],"classification":[119],"determine":[122],"current":[124],"stage":[125],"then":[129],"introduce":[130],"classifier":[132],"models.":[140],"Then":[141],"we":[142],"select":[143],"sets":[145],"modes":[149],"source":[152,162,183],"domains,":[155],"respectively.":[156],"In":[157],"our":[158,204],"approach,":[160],"are":[165],"regarded":[166],"biased":[168],"labels.":[171],"During":[172],"adaptive":[173],"training,":[174],"model":[176],"constantly":[177],"modifies":[178],"generates":[186],"pseudo-labels":[187],"unlabeled":[190],"data.":[193],"N-CMAPSS":[194],"dataset":[195],"selected":[197],"validation.":[199],"The":[200],"results":[201],"demonstrated":[202],"that":[203],"effectively":[208],"improve":[209],"accuracy":[211],"emphasizes":[216],"great":[218],"potential":[219],"PHM":[225],"studies.":[226]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
