{"id":"https://openalex.org/W7130567895","doi":"https://doi.org/10.1109/ictc66702.2025.11387994","title":"HyLME: Language Model Embeddings with Knowledge Distillation for Robust Predictive Maintenance under Missing Sensor Data","display_name":"HyLME: Language Model Embeddings with Knowledge Distillation for Robust Predictive Maintenance under Missing Sensor Data","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W7130567895","doi":"https://doi.org/10.1109/ictc66702.2025.11387994"},"language":null,"primary_location":{"id":"doi:10.1109/ictc66702.2025.11387994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11387994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"},"type":"conference-paper","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":null,"display_name":"Ju-Young Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I189442560","display_name":"Gyeongsang National University","ror":"https://ror.org/00saywf64","country_code":"KR","type":"education","lineage":["https://openalex.org/I189442560"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ju-Young Kim","raw_affiliation_strings":["Gyeongsang National University,Dept. of Computer Science and Engineering,Jinju,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gyeongsang National University,Dept. of Computer Science and Engineering,Jinju,Republic of Korea","institution_ids":["https://openalex.org/I189442560"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126381137","display_name":"Ji-Hong Park","orcid":null},"institutions":[{"id":"https://openalex.org/I189442560","display_name":"Gyeongsang National University","ror":"https://ror.org/00saywf64","country_code":"KR","type":"education","lineage":["https://openalex.org/I189442560"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ji-Hong Park","raw_affiliation_strings":["Gyeongsang National University,Dept. of Computer Science and Engineering,Jinju,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gyeongsang National University,Dept. of Computer Science and Engineering,Jinju,Republic of Korea","institution_ids":["https://openalex.org/I189442560"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122753600","display_name":"Gun-Woo Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I189442560","display_name":"Gyeongsang National University","ror":"https://ror.org/00saywf64","country_code":"KR","type":"education","lineage":["https://openalex.org/I189442560"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Gun-Woo Kim","raw_affiliation_strings":["Gyeongsang National University,Dept. of Computer Science and Engineering,Jinju,Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Gyeongsang National University,Dept. of Computer Science and Engineering,Jinju,Republic of Korea","institution_ids":["https://openalex.org/I189442560"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I189442560"],"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":"691","last_page":"696"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.20520000159740448,"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":0.20520000159740448,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.06019999831914902,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.04019999876618385,"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/missing-data","display_name":"Missing data","score":0.745199978351593},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.4747999906539917},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4731000065803528},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4481000006198883},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.42809998989105225},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3928999900817871},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.39100000262260437},{"id":"https://openalex.org/keywords/predictive-maintenance","display_name":"Predictive maintenance","score":0.3781999945640564},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.367900013923645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.746399998664856},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.745199978351593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6385999917984009},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6061999797821045},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4747999906539917},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4481000006198883},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.42809998989105225},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.367900013923645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36169999837875366},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C3018790387","wikidata":"https://www.wikidata.org/wiki/Q869010","display_name":"Hybrid learning","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.25220000743865967},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ictc66702.2025.11387994","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ictc66702.2025.11387994","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 16th International Conference on Information and Communication Technology Convergence (ICTC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4885404109954834,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2008056655","https://openalex.org/W2194775991","https://openalex.org/W2295598076","https://openalex.org/W2295797531","https://openalex.org/W2787894218","https://openalex.org/W2896457183","https://openalex.org/W3092012490","https://openalex.org/W3115694492","https://openalex.org/W3174086521","https://openalex.org/W3209249023","https://openalex.org/W4210930585","https://openalex.org/W4309905345","https://openalex.org/W4319988655","https://openalex.org/W4393062703","https://openalex.org/W4409647177","https://openalex.org/W4412889585"],"related_works":[],"abstract_inverted_index":{"In":[0,98,168],"the":[1,74,127,169,190,194,200],"4th":[2],"Industrial":[3],"Revolution,":[4],"predictive":[5,123],"maintenance":[6,124],"is":[7,39,133,186,203],"a":[8,40,103,107,112,134,156],"key":[9],"strategic":[10],"element":[11],"that":[12,46,138,199],"predicts":[13],"equipment":[14],"failure":[15],"or":[16],"status":[17],"in":[18,68,88,126,205,211],"real-time":[19],"by":[20],"collecting":[21],"relevant":[22],"data":[23,29,57,94,166,214],"from":[24,142,148],"sensors.":[25],"However,":[26],"missing":[27,56,130,165,213],"sensor":[28,212],"occurs":[30],"frequently,":[31],"leading":[32],"to":[33,118,163],"enormous":[34],"opportunity":[35],"costs.":[36],"Therefore,":[37],"there":[38],"growing":[41],"need":[42],"for":[43],"robust":[44,122,209],"models":[45,63,144],"can":[47],"maintain":[48],"high":[49],"accuracy":[50,178,192],"and":[51,79,95,111,121,145,208],"stable":[52],"performance":[53,185],"even":[54,125],"when":[55],"occurs.":[58],"Against":[59],"this":[60,99],"backdrop,":[61],"language":[62,70,143],"have":[64],"achieved":[65,175],"remarkable":[66],"success":[67],"natural":[69],"processing":[71],"tasks":[72],"through":[73],"innovative":[75],"architecture":[76,105,202],"of":[77,129,171,179,193],"Transformers,":[78],"their":[80],"context-based":[81],"learning":[82,109,136,151],"effect":[83],"has":[84],"been":[85],"widely":[86],"proven":[87],"other":[89],"domains":[90],"such":[91],"as":[92],"tabular":[93],"time-series":[96],"analysis.":[97],"paper,":[100],"we":[101,154],"propose":[102],"Hybrid":[104],"with":[106],"machine":[108,150],"teacher":[110],"Language":[113],"Model":[114],"Embedding-based":[115],"student":[116],"(HyLME)":[117],"enable":[119],"accurate":[120,207],"presence":[128],"data.":[131],"HyLME":[132,174],"hybrid":[135],"approach":[137],"fuses":[139],"text":[140],"embeddings":[141],"learns":[146],"knowledge":[147],"tree-based":[149],"models.":[152],"Additionally,":[153],"implemented":[155],"masking":[157],"scenario":[158],"based":[159],"on":[160],"feature":[161],"importance":[162],"simulate":[164],"conditions.":[167],"results":[170],"comparative":[172],"experiments,":[173],"an":[176],"average":[177,191],"0.83207":[180],"across":[181],"all":[182],"scenarios.":[183],"This":[184],"15.99%":[187],"higher":[188],"than":[189],"comparison":[195],"models,":[196],"which":[197],"indicates":[198],"proposed":[201],"effective":[204],"performing":[206],"predictions":[210],"situations.":[215]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-02-20T00:00:00"}
