{"id":"https://openalex.org/W4389331447","doi":"https://doi.org/10.1109/tii.2023.3332112","title":"Evidential Ensemble Preference-Guided Learning Approach for Real-Time Multimode Fault Diagnosis","display_name":"Evidential Ensemble Preference-Guided Learning Approach for Real-Time Multimode Fault Diagnosis","publication_year":2023,"publication_date":"2023-12-05","ids":{"openalex":"https://openalex.org/W4389331447","doi":"https://doi.org/10.1109/tii.2023.3332112"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2023.3332112","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2023.3332112","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-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/A5049730248","display_name":"Zeyi Liu","orcid":"https://orcid.org/0000-0003-2177-8906"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zeyi Liu","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2177-8906","affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066316185","display_name":"Chen Li","orcid":"https://orcid.org/0000-0003-2839-9335"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Li","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2839-9335","affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038690943","display_name":"Xiao He","orcid":"https://orcid.org/0000-0002-4588-0887"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao He","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4588-0887","affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5049730248"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":5.2543,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.96542022,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"20","issue":"4","first_page":"5495","last_page":"5504"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9991000294685364,"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/T12676","display_name":"Machine Learning and ELM","score":0.9991000294685364,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9983000159263611,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.7226852774620056},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6383898258209229},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5172024965286255},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5053241848945618},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.49985599517822266},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.479012668132782},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41893160343170166}],"concepts":[{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.7226852774620056},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6383898258209229},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5172024965286255},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5053241848945618},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.49985599517822266},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.479012668132782},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41893160343170166},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2023.3332112","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2023.3332112","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8400629925","display_name":null,"funder_award_id":"61733009","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W841144446","https://openalex.org/W1937143449","https://openalex.org/W2008573327","https://openalex.org/W2047028564","https://openalex.org/W2055576996","https://openalex.org/W2114541394","https://openalex.org/W2143288231","https://openalex.org/W2738226240","https://openalex.org/W2793318872","https://openalex.org/W2847284300","https://openalex.org/W2890126432","https://openalex.org/W2918460052","https://openalex.org/W2939985247","https://openalex.org/W3023056142","https://openalex.org/W3041091733","https://openalex.org/W3098804963","https://openalex.org/W3119535973","https://openalex.org/W4220787723","https://openalex.org/W4229063663","https://openalex.org/W4283837614","https://openalex.org/W4293731241","https://openalex.org/W4308485707","https://openalex.org/W4310299220","https://openalex.org/W4310675734","https://openalex.org/W4310875092","https://openalex.org/W4313326430","https://openalex.org/W4318953228","https://openalex.org/W4321033087","https://openalex.org/W4366378396","https://openalex.org/W4376481080","https://openalex.org/W4377224549","https://openalex.org/W4380359851","https://openalex.org/W4385267391","https://openalex.org/W6752866118"],"related_works":["https://openalex.org/W3147584709","https://openalex.org/W4376643315","https://openalex.org/W4324137541","https://openalex.org/W2900445707","https://openalex.org/W4285741730","https://openalex.org/W1191482210","https://openalex.org/W4285046548","https://openalex.org/W4210302090","https://openalex.org/W3092276832","https://openalex.org/W4375951447"],"abstract_inverted_index":{"Operational":[0],"changes":[1],"in":[2,38],"industrial":[3],"production":[4],"can":[5],"alter":[6],"system":[7],"operating":[8,82,148],"modes,":[9],"which":[10,34],"complicates":[11],"real-time":[12,55,125],"fault":[13,20,24,31,57,78,127],"diagnosis":[14,25,58,128],"by":[15,122],"affecting":[16],"sensor":[17],"data":[18],"and":[19,135,150],"characteristics.":[21],"In":[22],"addition,":[23],"tasks":[26],"encounter":[27],"the":[28,39,60,70,86,89,109,112,131],"challenge":[29],"of":[30,41,77,88,117],"feature":[32],"drift,":[33],"causes":[35],"a":[36,51,94,124],"decline":[37],"performance":[40],"previously":[42],"trained":[43],"models":[44],"on":[45,85],"new":[46],"data.":[47],"This":[48],"article":[49],"presents":[50],"novel":[52],"approach":[53,64,119,145],"for":[54],"multimode":[56,126],"called":[59],"evidential":[61,104],"ensemble":[62,75],"preference-guided":[63],"to":[65,107],"tackle":[66],"these":[67],"issues.":[68],"During":[69],"offline":[71],"stage,":[72],"we":[73],"extract":[74],"preferences":[76,110],"information":[79],"across":[80],"different":[81],"modes":[83,149],"based":[84],"structure":[87],"broad":[90],"learning":[91],"system.":[92],"Subsequently,":[93],"parameter":[95],"iterative":[96],"update":[97],"rule":[98],"is":[99,120],"developed":[100],"that":[101,142],"utilizes":[102],"an":[103],"reasoning":[105],"technique":[106],"emphasize":[108],"during":[111],"online":[113],"stage.":[114],"The":[115,139],"effectiveness":[116],"our":[118,143],"evaluated":[121],"constructing":[123],"dataset":[129],"using":[130],"Tennessee":[132],"Eastman":[133],"process":[134],"conducting":[136],"multiple":[137],"experiments.":[138],"results":[140],"demonstrate":[141],"proposed":[144],"effectively":[146],"identifies":[147],"diagnoses":[151],"faults":[152],"simultaneously,":[153],"surpassing":[154],"existing":[155],"advanced":[156],"methods.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":8}],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2025-10-10T00:00:00"}