{"id":"https://openalex.org/W7135088134","doi":"https://doi.org/10.48550/arxiv.2603.10430","title":"Domain-Adaptive Health Indicator Learning with Degradation-Stage Synchronized Sampling and Cross-Domain Autoencoder","display_name":"Domain-Adaptive Health Indicator Learning with Degradation-Stage Synchronized Sampling and Cross-Domain Autoencoder","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135088134","doi":"https://doi.org/10.48550/arxiv.2603.10430"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.10430","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10430","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.10430","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128917020","display_name":"Jungho Choo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choo, Jungho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011132246","display_name":"Hanbyeol Park","orcid":"https://orcid.org/0009-0003-0919-5764"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Hanbyeol","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042446425","display_name":"Gawon Lee","orcid":"https://orcid.org/0000-0002-8554-9953"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Gawon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019764220","display_name":"Yunkyung Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Yunkyung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128905736","display_name":"Hyerim Bae","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bae, Hyerim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.7204999923706055,"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":0.7204999923706055,"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":0.05119999870657921,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.04430000111460686,"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/prognostics","display_name":"Prognostics","score":0.8165000081062317},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7958999872207642},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5192999839782715},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47929999232292175},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.47870001196861267},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.47519999742507935},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4706000089645386},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4659999907016754}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.8165000081062317},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7958999872207642},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6532999873161316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6326000094413757},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5192999839782715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48539999127388},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47999998927116394},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47929999232292175},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.47870001196861267},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.47519999742507935},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4706000089645386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4659999907016754},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C135510737","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance indicator","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.28349998593330383},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2743000090122223},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.2628999948501587},{"id":"https://openalex.org/C121272143","wikidata":"https://www.wikidata.org/wiki/Q17021791","display_name":"Health indicator","level":3,"score":0.2572999894618988}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.10430","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10430","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.10430","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10430","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.41140979528427124,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,145],"construction":[1],"of":[2,53,70,167],"high":[3],"quality":[4],"health":[5,14],"indicators":[6],"(HIs)":[7],"is":[8,39],"crucial":[9],"for":[10,189],"effective":[11],"prognostics":[12],"and":[13,65,98,118,157],"management.":[15],"Although":[16,36],"deep":[17],"learning":[18],"has":[19],"significantly":[20],"advanced":[21],"HI":[22],"modeling,":[23],"existing":[24],"approaches":[25],"often":[26],"struggle":[27],"with":[28,137],"distribution":[29],"mismatches":[30],"resulting":[31,60],"from":[32],"varying":[33],"operating":[34],"conditions.":[35],"domain":[37],"adaptation":[38],"typically":[40],"employed":[41],"to":[42,111,140],"mitigate":[43],"these":[44,84],"shifts,":[45],"two":[46],"critical":[47],"challenges":[48],"remain:":[49],"(1)":[50],"the":[51,67,99,158],"misalignment":[52],"degradation":[54,92,113],"stages":[55],"during":[56,127],"random":[57],"mini-batch":[58],"sampling,":[59,182],"in":[61,73],"misleading":[62],"discrepancy":[63],"losses,":[64],"(2)":[66],"structural":[68],"limitations":[69],"small-kernel":[71],"1D-CNNs":[72],"capturing":[74],"long-range":[75],"temporal":[76,134],"dependencies":[77],"within":[78],"complex":[79],"vibration":[80],"signals.":[81],"To":[82],"address":[83],"issues,":[85],"we":[86],"propose":[87],"a":[88,152,186],"domain-adaptive":[89],"framework":[90,147],"comprising":[91],"stage":[93],"synchronized":[94,122],"batch":[95],"sampling":[96],"(DSSBS)":[97],"cross-domain":[100,178],"aligned":[101],"fusion":[102],"large":[103],"autoencoder":[104],"(CAFLAE).":[105],"DSSBS":[106,176],"utilizes":[107],"kernel":[108],"change-point":[109],"detection":[110],"segment":[112],"stages,":[114],"ensuring":[115],"that":[116,175],"source":[117],"target":[119],"mini-batches":[120],"are":[121],"by":[123],"their":[124],"failure":[125],"phases":[126],"alignment.":[128],"Complementing":[129],"this,":[130],"CAFLAE":[131,184],"integrates":[132],"large-kernel":[133],"feature":[135],"extraction":[136],"cross-attention":[138],"mechanisms":[139],"learn":[141],"superior":[142],"domain-invariant":[143],"representations.":[144],"proposed":[146],"was":[148],"rigorously":[149],"validated":[150],"on":[151],"Korean":[153],"defense":[154],"system":[155],"dataset":[156],"XJTU-SY":[159],"bearing":[160],"dataset,":[161],"achieving":[162],"an":[163],"average":[164],"performance":[165],"enhancement":[166],"24.1%":[168],"over":[169],"state-of-the-art":[170],"methods.":[171],"These":[172],"results":[173],"demonstrate":[174],"improves":[177],"alignment":[179],"through":[180],"stage-consistent":[181],"whereas":[183],"offers":[185],"high-performance":[187],"backbone":[188],"long-term":[190],"industrial":[191],"condition":[192],"monitoring.":[193]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-13T00:00:00"}