{"id":"https://openalex.org/W7163652822","doi":"https://doi.org/10.48550/arxiv.2606.05481","title":"Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models","display_name":"Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163652822","doi":"https://doi.org/10.48550/arxiv.2606.05481"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.05481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05481","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.2606.05481","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137977826","display_name":"Raffael Theiler","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Theiler, Raffael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085617225","display_name":"Lev Telyatnikov","orcid":"https://orcid.org/0009-0008-0922-8032"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Telyatnikov, Lev","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138010135","display_name":"Leandro Von Krannichfeldt","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Von Krannichfeldt, Leandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137986801","display_name":"Olga Fink","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fink, Olga","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":0.8986999988555908,"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":0.8986999988555908,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.015300000086426735,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.008999999612569809,"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":0.9585999846458435},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5835999846458435},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.566100001335144},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.42800000309944153},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4043000042438507},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4004000127315521},{"id":"https://openalex.org/keywords/predictive-maintenance","display_name":"Predictive maintenance","score":0.32519999146461487}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.9585999846458435},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5835999846458435},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.566100001335144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.482699990272522},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.41690000891685486},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3937999904155731},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3734999895095825},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.27959999442100525},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2615000009536743},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.258899986743927},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2574000060558319},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C171265473","wikidata":"https://www.wikidata.org/wiki/Q5691117","display_name":"Health management system","level":3,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.05481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05481","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.2606.05481","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.05481","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Data-driven":[0],"Prognostics":[1],"and":[2,14,38,63,90,121,124,138,160,185,205],"Health":[3],"Management":[4],"(PHM)":[5],"uses":[6],"time-varying":[7],"condition-monitoring":[8],"data":[9,32,127],"to":[10,26,83],"diagnose":[11],"system":[12],"states":[13],"estimate":[15],"remaining":[16],"useful":[17],"life":[18],"in":[19,172,181],"engineered":[20],"assets.":[21],"These":[22,195],"tasks":[23,117],"are":[24,33,59,125,170],"central":[25],"maintenance":[27],"planning,":[28],"but":[29],"industrial":[30,84],"PHM":[31,98,210],"often":[34],"fragmented,":[35],"partially":[36],"observed,":[37],"poorly":[39],"labeled,":[40],"which":[41],"hinders":[42],"supervised":[43],"learning.":[44],"Foundation":[45,81],"models":[46,58,112,152,169,201],"offer":[47],"a":[48,76,95,142,203],"route":[49],"toward":[50],"reusable":[51],"predictive":[52],"systems,":[53],"yet":[54],"most":[55],"time-series":[56],"foundation":[57,151,200],"designed":[60],"for":[61,78,208],"forecasting":[62],"assume":[64],"long,":[65],"coherent,":[66],"regularly":[67],"sampled":[68],"sequences.":[69],"To":[70],"address":[71],"this":[72],"gap,":[73],"we":[74,91,108],"propose":[75],"framework":[77],"applying":[79],"Tabular":[80],"Models":[82],"time":[85],"series":[86],"using":[87],"in-context":[88],"learning,":[89],"evaluate":[92],"them":[93,131],"on":[94,189],"variety":[96],"of":[97],"tasks.":[99,162],"By":[100],"converting":[101],"raw":[102],"unit-level":[103],"signals":[104],"into":[105],"tabular":[106,150,183,199],"rows,":[107],"show":[109,166],"that":[110,149,167,175,186,198],"these":[111],"perform":[113],"well":[114],"across":[115,158],"multiple":[116],"-":[118,123],"including":[119],"prognostics,":[120],"diagnostics":[122],"highly":[126],"efficient.":[128],"We":[129],"compare":[130],"directly":[132],"with":[133],"sequence":[134],"models,":[135],"transformer":[136],"baselines,":[137],"gradient-boosted":[139],"trees":[140],"under":[141,193],"common":[143],"evaluation":[144],"protocol.":[145],"The":[146],"results":[147,196],"indicate":[148],"achieve":[153],"the":[154,182],"best":[155],"average":[156],"ranks":[157],"prognostic":[159],"diagnostic":[161],"Our":[163],"findings":[164],"further":[165],"PFN-based":[168],"competitive":[171],"low-data":[173],"regimes,":[174],"temporal":[176],"context":[177,191],"can":[178],"be":[179],"preserved":[180],"representation,":[184],"performance":[187],"depends":[188],"representative":[190],"construction":[192],"subsampling.":[194],"demonstrate":[197],"provide":[202],"practical":[204],"general":[206],"interface":[207],"heterogeneous":[209],"problems.":[211]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-06T00:00:00"}
