{"id":"https://openalex.org/W7224748336","doi":"https://doi.org/10.1002/qre.70468","title":"Reliability\u2010Guided Recovery and Relation Learning for Fault Diagnosis in Coupled Industrial Processes","display_name":"Reliability\u2010Guided Recovery and Relation Learning for Fault Diagnosis in Coupled Industrial Processes","publication_year":2026,"publication_date":"2026-10-30","ids":{"openalex":"https://openalex.org/W7224748336","doi":"https://doi.org/10.1002/qre.70468"},"language":"en","primary_location":{"id":"doi:10.1002/qre.70468","doi":"https://doi.org/10.1002/qre.70468","is_oa":false,"landing_page_url":"https://doi.org/10.1002/qre.70468","pdf_url":null,"source":{"id":"https://openalex.org/S165633816","display_name":"Quality and Reliability Engineering International","issn_l":"0748-8017","issn":["0748-8017","1099-1638"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Quality and Reliability Engineering International","raw_type":"journal-article"},"volume":null,"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/A5074358139","display_name":"Zikang Chen","orcid":"https://orcid.org/0009-0006-2418-1709","observed_orcids":["https://orcid.org/0009-0006-2418-1709","https://orcid.org/0009-0007-9234-2989"]},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zikang Chen","raw_affiliation_strings":["Jiangsu Co\u2010Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry University  Nanjing China"],"raw_orcid":"https://orcid.org/0009-0007-9234-2989","affiliations":[{"raw_affiliation_string":"Jiangsu Co\u2010Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry University  Nanjing China","institution_ids":["https://openalex.org/I167027274"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104320393","display_name":"Tong Hu","orcid":"https://orcid.org/0009-0004-0217-1943","observed_orcids":["https://orcid.org/0009-0004-0217-1943"]},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong Hu","raw_affiliation_strings":["Jiangsu Co\u2010Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry University  Nanjing China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Co\u2010Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry University  Nanjing China","institution_ids":["https://openalex.org/I167027274"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148161822","display_name":"Yue Bai","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Bai","raw_affiliation_strings":["Jiangsu Co\u2010Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry University  Nanjing China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jiangsu Co\u2010Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry University  Nanjing China","institution_ids":["https://openalex.org/I167027274"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160223532","display_name":"Wenguang Wei","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenguang Wei","raw_affiliation_strings":["Shandong Huatai Paper Co., Ltd  Dongying China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Huatai Paper Co., Ltd  Dongying China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160160692","display_name":"Peng Huang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng Huang","raw_affiliation_strings":["Shandong Huatai Paper Co., Ltd  Dongying China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Huatai Paper Co., Ltd  Dongying China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148116337","display_name":"Yutong Wei","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yutong Wei","raw_affiliation_strings":["UNSW Business School University of New South Wales  Sydney New South Wales Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UNSW Business School University of New South Wales  Sydney New South Wales Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100408236","display_name":"Hongbin Liu","orcid":"https://orcid.org/0000-0001-9645-686X","observed_orcids":["https://orcid.org/0000-0001-9645-686X"]},"institutions":[{"id":"https://openalex.org/I167027274","display_name":"Nanjing Forestry University","ror":"https://ror.org/03m96p165","country_code":"CN","type":"education","lineage":["https://openalex.org/I167027274"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongbin Liu","raw_affiliation_strings":["Jiangsu Co\u2010Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry University  Nanjing China"],"raw_orcid":"https://orcid.org/0000-0001-9645-686X","affiliations":[{"raw_affiliation_string":"Jiangsu Co\u2010Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry University  Nanjing China","institution_ids":["https://openalex.org/I167027274"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100408236"],"corresponding_institution_ids":["https://openalex.org/I167027274"],"apc_list":{"value":4550,"currency":"USD","value_usd":4550},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81604429,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10876","display_name":"Fault Detection and Control Systems","score":1.0,"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/T10876","display_name":"Fault Detection and Control Systems","score":1.0,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.0,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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-diagnosis","display_name":"fault diagnosis","score":0.9900000095367432},{"id":"https://openalex.org/keywords/feature-recovery","display_name":"feature recovery","score":0.7239999771118164},{"id":"https://openalex.org/keywords/relation-learning","display_name":"relation learning","score":0.9570000171661377},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.796999990940094},{"id":"https://openalex.org/keywords/multi-scale-reconstruction","display_name":"multi-scale reconstruction","score":0.5429999828338623},{"id":"https://openalex.org/keywords/graph-convolution","display_name":"graph convolution","score":0.5630000233650208},{"id":"https://openalex.org/keywords/measurement-disturbance","display_name":"measurement disturbance","score":0.6660000085830688},{"id":"https://openalex.org/keywords/papermaking","display_name":"papermaking","score":0.6460000276565552},{"id":"https://openalex.org/keywords/benchmark-simulation-model","display_name":"benchmark simulation model","score":0.6949999928474426}],"concepts":[{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6545000076293945},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.544700026512146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5128999948501587},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4936999976634979},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.48660001158714294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4742000102996826},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.46970000863075256},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.45680001378059387},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4316999912261963},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.39480000734329224},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3853999972343445},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38019999861717224},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3255999982357025},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.25440001487731934},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/qre.70468","doi":"https://doi.org/10.1002/qre.70468","is_oa":false,"landing_page_url":"https://doi.org/10.1002/qre.70468","pdf_url":null,"source":{"id":"https://openalex.org/S165633816","display_name":"Quality and Reliability Engineering International","issn_l":"0748-8017","issn":["0748-8017","1099-1638"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-70","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Quality and Reliability Engineering International","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1966863755","https://openalex.org/W2741385306","https://openalex.org/W2969739423","https://openalex.org/W3182954817","https://openalex.org/W3198059351","https://openalex.org/W4281636081","https://openalex.org/W4285294533","https://openalex.org/W4292264860","https://openalex.org/W4309486871","https://openalex.org/W4313524849","https://openalex.org/W4315783815","https://openalex.org/W4319335604","https://openalex.org/W4384008706","https://openalex.org/W4385407859","https://openalex.org/W4387794318","https://openalex.org/W4388558401","https://openalex.org/W4389670015","https://openalex.org/W4390237272","https://openalex.org/W4392397350","https://openalex.org/W4393972863","https://openalex.org/W4394676404","https://openalex.org/W4396242602","https://openalex.org/W4396704856","https://openalex.org/W4400438563","https://openalex.org/W4401556301","https://openalex.org/W4406610523","https://openalex.org/W4407736561","https://openalex.org/W4409991013","https://openalex.org/W4410056346","https://openalex.org/W4410358711","https://openalex.org/W4410537656","https://openalex.org/W4410554764","https://openalex.org/W4411019560","https://openalex.org/W4411270300","https://openalex.org/W4413104875","https://openalex.org/W4414110835"],"related_works":["https://openalex.org/W1992078537","https://openalex.org/W7202369187","https://openalex.org/W7163161291","https://openalex.org/W4406522967","https://openalex.org/W7167076921","https://openalex.org/W2274213435","https://openalex.org/W2654548645","https://openalex.org/W7086592800","https://openalex.org/W4220848166","https://openalex.org/W4390604576"],"abstract_inverted_index":{"ABSTRACT":[0],"Reliable":[1],"fault":[2],"diagnosis":[3],"supports":[4],"stable":[5],"operation":[6],"and":[7,18,25,50,59,63,88,97,111,129,148,155],"product":[8],"quality":[9],"in":[10],"complex":[11],"industrial":[12,173],"processes.":[13,174],"Process":[14],"coupling,":[15],"operating":[16],"variability":[17],"measurement":[19,160],"disturbances":[20],"influence":[21],"both":[22],"observed":[23],"features":[24,62],"their":[26],"interdependencies,":[27],"motivating":[28],"the":[29,106,115,133,140],"integration":[30],"of":[31,153],"feature":[32,102],"recovery":[33,47,55,161],"with":[34,48,150,162],"relation":[35,52,163],"learning.":[36,53],"This":[37],"study":[38],"proposes":[39],"RGIFormer,":[40],"a":[41,65,81],"framework":[42,134,158],"that":[43],"couples":[44],"adaptive":[45],"multi\u2010scale":[46],"global":[49,60,86],"sample\u2010adaptive":[51,89],"The":[54,72,157],"module":[56],"combines":[57],"local":[58],"reconstruction":[61],"uses":[64],"reconstruction\u2010agreement":[66],"gate":[67,83],"to":[68,104,165],"regulate":[69],"input":[70],"correction.":[71],"corrected":[73],"inputs":[74],"then":[75],"support":[76],"dual\u2010relation":[77],"graph":[78,95],"learning,":[79],"where":[80],"learned":[82],"balances":[84],"shared":[85],"associations":[87],"symmetric":[90],"relations.":[91],"These":[92],"relations":[93],"guide":[94],"convolution":[96],"grouped\u2010descriptor":[98],"attention,":[99],"allowing":[100],"recovered":[101],"information":[103],"shape":[105],"diagnostic":[107,169],"representation.":[108],"Feature":[109],"fusion":[110],"prototype\u2010supported":[112],"scoring":[113],"combine":[114],"resulting":[116],"representations":[117],"for":[118,171],"classification.":[119],"Experiments":[120],"on":[121],"Benchmark":[122],"Simulation":[123],"Model":[124],"No.":[125],"2":[126],"(BSM2)":[127],"snapshots":[128],"papermaking":[130],"descriptors":[131],"evaluate":[132],"across":[135],"five":[136],"random":[137],"seeds.":[138],"Under":[139],"original":[141],"dataset\u2010specific":[142],"splits,":[143],"mean":[144],"accuracies":[145],"are":[146],"78.81%":[147],"93.66%,":[149],"macro\u2010F1":[151],"scores":[152],"77.76%":[154],"93.78%.":[156],"connects":[159],"construction":[164],"provide":[166],"an":[167],"integrated":[168],"representation":[170],"coupled":[172]},"counts_by_year":[],"updated_date":"2026-11-01T11:40:01.658920","created_date":"2026-10-31T00:00:00"}