{"id":"https://openalex.org/W6963350507","doi":"https://doi.org/10.21227/6f2m-xv75","title":"Fault Diagnosis of Blast Furnace Iron-making Process with A Novel Deep Stationary Kernel Support Vector Machine Approach","display_name":"Fault Diagnosis of Blast Furnace Iron-making Process with A Novel Deep Stationary Kernel Support Vector Machine Approach","publication_year":2022,"publication_date":"2022-04-04","ids":{"openalex":"https://openalex.org/W6963350507","doi":"https://doi.org/10.21227/6f2m-xv75"},"language":"en","primary_location":{"id":"doi:10.21227/6f2m-xv75","is_oa":true,"landing_page_url":"https://doi.org/10.21227/6f2m-xv75","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/6f2m-xv75","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lou, Siwei","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lou, Siwei","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"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":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6347000002861023},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5845999717712402},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5587999820709229},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5335999727249146},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.49410000443458557},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.4555000066757202},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.45019999146461487},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4431999921798706},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.43160000443458557}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6347000002861023},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5845999717712402},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5587999820709229},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5335999727249146},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.49410000443458557},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48399999737739563},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4555000066757202},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.45019999146461487},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4431999921798706},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C183560197","wikidata":"https://www.wikidata.org/wiki/Q7247302","display_name":"Process state","level":3,"score":0.4077000021934509},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C155386361","wikidata":"https://www.wikidata.org/wiki/Q1649571","display_name":"Process control","level":3,"score":0.3903999924659729},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3903999924659729},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.3869999945163727},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3521000146865845},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3357999920845032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3264000117778778},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3156000077724457},{"id":"https://openalex.org/C2780269488","wikidata":"https://www.wikidata.org/wiki/Q181485","display_name":"Blast furnace","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2962000072002411},{"id":"https://openalex.org/C110405555","wikidata":"https://www.wikidata.org/wiki/Q1192209","display_name":"Stationary process","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.26669999957084656},{"id":"https://openalex.org/C80038721","wikidata":"https://www.wikidata.org/wiki/Q4380673","display_name":"Process safety","level":3,"score":0.26510000228881836},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/6f2m-xv75","is_oa":true,"landing_page_url":"https://doi.org/10.21227/6f2m-xv75","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"dataset"}],"best_oa_location":{"id":"doi:10.21227/6f2m-xv75","is_oa":true,"landing_page_url":"https://doi.org/10.21227/6f2m-xv75","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"blast":[1],"furnace":[2],"iron-making":[3,15],"process":[4,16,19,39],"(BFIP),":[5],"there":[6],"is":[7,52,108,177],"a":[8,13,79,117],"significant":[9,38],"push":[10],"to":[11,46,54,62,110,123,142,148,191],"maintain":[12],"stable":[14,59],"and":[17,58,134,176,185],"ensure":[18],"at":[20],"maximum":[21],"efficiency.":[22],"While":[23],"some":[24,37],"control":[25],"systems":[26],"can":[27,70],"compensate":[28],"for":[29,90],"multiple":[30],"types":[31,147],"of":[32,99,165,182],"disturbances":[33],"when":[34],"faults":[35,40,66],"occur,":[36],"often":[41],"require":[42],"precise":[43],"human":[44],"intervention":[45],"avoid":[47],"safety":[48],"hazards.":[49],"Therefore,":[50],"it":[51],"crucial":[53],"develop":[55],"an":[56,155],"efficient":[57],"diagnostic":[60],"system":[61],"efficiently":[63],"identify":[64],"these":[65],"so":[67],"that":[68,179],"operators":[69],"deal":[71],"with":[72],"them":[73],"quickly.":[74],"This":[75],"paper":[76],"focuses":[77],"on":[78,102,160],"novel":[80],"approach":[81],"called":[82],"deep":[83,120,125],"stationary":[84,104,183],"kernel":[85,121],"support":[86,130],"vector":[87,131],"machine":[88],"(DSKSVM)":[89],"nonstationary":[91,100],"BFIP":[92,161],"fault":[93,150,174],"diagnosis.":[94],"To":[95],"eliminate":[96],"the":[97,163,180],"impact":[98],"property":[101],"modeling,":[103],"subspace":[105],"analysis":[106],"(SSA)":[107],"adopted":[109],"estimate":[111],"consistent":[112],"underlying":[113],"features.":[114],"Then,":[115],"design":[116],"multi-layer":[118],"stacked":[119],"network":[122],"explore":[124],"nonlinear":[126],"information":[127],"further.":[128],"A":[129],"machine-based":[132],"classifier":[133],"corresponding":[135],"two-tier":[136],"model":[137],"optimization":[138],"algorithm":[139],"are":[140,189],"constructed":[141],"isolate":[143],"data":[144],"from":[145],"different":[146],"achieve":[149],"diagnosis":[151],"task.":[152],"At":[153],"last,":[154],"actual":[156],"case":[157],"study":[158],"based":[159],"presents":[162],"effectiveness":[164],"DSKSVM.":[166],"The":[167],"proposed":[168],"method":[169],"has":[170],"outstanding":[171],"results":[172],"in":[173],"diagnosis,":[175],"verified":[178],"performances":[181],"construction":[184],"online":[186],"computation":[187],"times":[188],"superior":[190],"other":[192],"methods.":[193]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}