{"id":"https://openalex.org/W7133194965","doi":"https://doi.org/10.1093/jcde/qwag017","title":"Jointly optimized semi-supervised graph embedding stochastic configuration networks for industrial fault diagnosis","display_name":"Jointly optimized semi-supervised graph embedding stochastic configuration networks for industrial fault diagnosis","publication_year":2026,"publication_date":"2026-02-28","ids":{"openalex":"https://openalex.org/W7133194965","doi":"https://doi.org/10.1093/jcde/qwag017"},"language":"en","primary_location":{"id":"doi:10.1093/jcde/qwag017","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwag017","pdf_url":null,"source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1093/jcde/qwag017","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030752272","display_name":"Panliang Yuan","orcid":"https://orcid.org/0000-0003-0227-020X"},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Panliang Yuan","raw_affiliation_strings":["State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,"],"raw_orcid":"https://orcid.org/0000-0003-0227-020X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,","institution_ids":["https://openalex.org/I178232147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127832820","display_name":"Chenglong Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglong Zhang","raw_affiliation_strings":["School of Data Science, The Chinese University of Hong Kong , Shenzhen 518172 ,"],"raw_orcid":"https://orcid.org/0009-0003-0002-5128","affiliations":[{"raw_affiliation_string":"School of Data Science, The Chinese University of Hong Kong , Shenzhen 518172 ,","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127843539","display_name":"Shaobo Li","orcid":null},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]},{"id":"https://openalex.org/I4210156872","display_name":"Guizhou Institute of Technology","ror":"https://ror.org/05x510r30","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210156872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaobo Li","raw_affiliation_strings":["College of Big Data, Guizhou Institute of Technology , Guiyang 550001 ,","State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,"],"raw_orcid":"https://orcid.org/0000-0003-4759-6000","affiliations":[{"raw_affiliation_string":"College of Big Data, Guizhou Institute of Technology , Guiyang 550001 ,","institution_ids":["https://openalex.org/I4210156872"]},{"raw_affiliation_string":"State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,","institution_ids":["https://openalex.org/I178232147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114224014","display_name":"Zihao Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihao Liao","raw_affiliation_strings":["State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,"],"raw_orcid":"https://orcid.org/0000-0002-1533-1828","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,","institution_ids":["https://openalex.org/I178232147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127864281","display_name":"Yang Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]},{"id":"https://openalex.org/I4210150300","display_name":"Guzhou Transportation Planning Survey & Design Academe (China)","ror":"https://ror.org/048yeyk79","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210150300"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Hu","raw_affiliation_strings":["Automotive Engineering, Guizhou Traffic Technician and Transportation College , Guiyang 550008 ,","State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,"],"raw_orcid":"https://orcid.org/0009-0003-8061-9195","affiliations":[{"raw_affiliation_string":"Automotive Engineering, Guizhou Traffic Technician and Transportation College , Guiyang 550008 ,","institution_ids":["https://openalex.org/I4210150300"]},{"raw_affiliation_string":"State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,","institution_ids":["https://openalex.org/I178232147"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005199779","display_name":"Fengbin Wu","orcid":"https://orcid.org/0000-0001-6094-8221"},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengbin Wu","raw_affiliation_strings":["State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,"],"raw_orcid":"https://orcid.org/0000-0001-6094-8221","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 ,","institution_ids":["https://openalex.org/I178232147"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5030752272"],"corresponding_institution_ids":["https://openalex.org/I178232147"],"apc_list":{"value":1650,"currency":"USD","value_usd":1650},"apc_paid":{"value":1650,"currency":"USD","value_usd":1650},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31791278,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"3","first_page":"246","last_page":"261"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.2978000044822693,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.2978000044822693,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.17069999873638153,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.05770000070333481,"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/embedding","display_name":"Embedding","score":0.7386000156402588},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6054999828338623},{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.4973999857902527},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48399999737739563},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4797999858856201},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.476500004529953},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.46380001306533813},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.4406000077724457}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7386000156402588},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6195999979972839},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6054999828338623},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48399999737739563},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4797999858856201},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.476500004529953},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.46380001306533813},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.4406000077724457},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.439300000667572},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4113999903202057},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3978999853134155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37070000171661377},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.36809998750686646},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.328900009393692},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.29739999771118164},{"id":"https://openalex.org/C153120616","wikidata":"https://www.wikidata.org/wiki/Q17068315","display_name":"Manifold alignment","level":4,"score":0.29660001397132874},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28790000081062317},{"id":"https://openalex.org/C194387892","wikidata":"https://www.wikidata.org/wiki/Q1747770","display_name":"Stochastic optimization","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.26330000162124634}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1093/jcde/qwag017","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwag017","pdf_url":null,"source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1093/jcde/qwag017","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwag017","pdf_url":null,"source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5198919177055359}],"awards":[{"id":"https://openalex.org/G2580308691","display_name":null,"funder_award_id":"2023YFB3308802","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8847286764","display_name":null,"funder_award_id":"52275480","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1964940342","https://openalex.org/W2004186751","https://openalex.org/W2012638612","https://openalex.org/W2290122975","https://openalex.org/W2593382986","https://openalex.org/W2618530766","https://openalex.org/W2686240873","https://openalex.org/W2991207885","https://openalex.org/W3090070438","https://openalex.org/W3156969875","https://openalex.org/W3174796347","https://openalex.org/W4200312990","https://openalex.org/W4213019189","https://openalex.org/W4312924263","https://openalex.org/W4319983506","https://openalex.org/W4320005588","https://openalex.org/W4321016476","https://openalex.org/W4362500637","https://openalex.org/W4365128558","https://openalex.org/W4366281532","https://openalex.org/W4383913315","https://openalex.org/W4386059959","https://openalex.org/W4390949012","https://openalex.org/W4398142493","https://openalex.org/W4399375885","https://openalex.org/W4401308145","https://openalex.org/W4402027615","https://openalex.org/W4402260192","https://openalex.org/W4403590888","https://openalex.org/W4405238643","https://openalex.org/W4405388624","https://openalex.org/W4408129641","https://openalex.org/W4409346038","https://openalex.org/W4410089099","https://openalex.org/W4410582084","https://openalex.org/W4412030320","https://openalex.org/W4412373256","https://openalex.org/W4414144469","https://openalex.org/W4414308293","https://openalex.org/W4414318847","https://openalex.org/W4415220926","https://openalex.org/W4416662593","https://openalex.org/W7083453042","https://openalex.org/W7083708274","https://openalex.org/W7101390280","https://openalex.org/W7117713270"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"We":[1,23,60,93],"address":[2],"industrial":[3,98,175],"fault":[4],"diagnosis":[5],"under":[6],"scarce":[7],"labels":[8],"and":[9,41,44,85,131],"strict":[10],"training-time":[11],"constraints,":[12],"a":[13,63,169],"setting":[14],"where":[15],"many":[16],"existing":[17],"methods":[18],"implicitly":[19],"assume":[20],"abundant":[21],"annotations.":[22],"propose":[24],"the":[25,49,90],"jointly":[26,123],"optimized":[27,124],"semi-supervised":[28,117,125],"graph-embedded":[29],"stochastic":[30,113,134,166],"configuration":[31,114,135,167],"network":[32,111,115],"(JOSGESCN).":[33],"The":[34],"method":[35],"constructs":[36],"geometric":[37],"similarity":[38],"between":[39],"labelled":[40],"unlabelled":[42,58],"samples":[43],"injects":[45],"this":[46],"structure":[47],"into":[48],"model":[50],"via":[51],"manifold":[52,162],"regularization,":[53],"enabling":[54],"effective":[55],"use":[56],"of":[57,89],"data.":[59],"further":[61],"introduce":[62],"supervisory":[64],"mechanism":[65],"that":[66,159],"adaptively":[67],"configures":[68],"hidden":[69],"nodes":[70],"to":[71],"strengthen":[72],"representation":[73],"capacity.":[74],"A":[75],"joint":[76],"optimization":[77],"strategy":[78],"reduces":[79],"error":[80],"propagation":[81],"from":[82],"one-shot":[83],"pseudo-labelling":[84],"encourages":[86],"low-density":[87],"separation":[88],"decision":[91],"boundary.":[92],"evaluate":[94],"JOSGESCN":[95,141],"on":[96],"three":[97],"fault-diagnosis":[99],"data":[100,139],"sets":[101],"against":[102],"support":[103],"vector":[104,108,119,127],"machine":[105],"(SVM),":[106],"random":[107,118,126],"functional":[109,120,128],"link":[110,121,129],"(RVFL),":[112],"(SCN),":[116],"(SSRVFL),":[122],"(JOSRVFL),":[130],"LPSCN":[132],"(locality-preserving":[133],"network).":[136],"Across":[137],"all":[138],"sets,":[140],"delivers":[142],"superior":[143],"accuracy,":[144],"demonstrating":[145],"consistent":[146],"gains":[147],"over":[148],"strong":[149],"baselines":[150],"while":[151],"maintaining":[152],"practical":[153],"training":[154],"efficiency.":[155],"These":[156],"results":[157],"indicate":[158],"coupling":[160],"graph-based":[161],"priors":[163],"with":[164],"adaptive":[165],"is":[168],"promising":[170],"direction":[171],"for":[172],"label-efficient,":[173],"high-performance":[174],"health":[176],"monitoring.":[177]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-03-03T00:00:00"}
