{"id":"https://openalex.org/W4416650049","doi":"https://doi.org/10.1109/tcyb.2025.3634611","title":"Dynamic Causal Entropy-Spatiotemporal Convolutional Network for Quality-Related Fault Diagnosis of Large-Scale Industrial Processes","display_name":"Dynamic Causal Entropy-Spatiotemporal Convolutional Network for Quality-Related Fault Diagnosis of Large-Scale Industrial Processes","publication_year":2025,"publication_date":"2025-11-25","ids":{"openalex":"https://openalex.org/W4416650049","doi":"https://doi.org/10.1109/tcyb.2025.3634611","pmid":"https://pubmed.ncbi.nlm.nih.gov/41289112"},"language":"en","primary_location":{"id":"doi:10.1109/tcyb.2025.3634611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2025.3634611","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100570942","display_name":"Dongjie Hua","orcid":"https://orcid.org/0009-0005-2520-301X"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dongjie Hua","raw_affiliation_strings":["Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100459936","display_name":"Jie Dong","orcid":"https://orcid.org/0000-0001-7585-6637"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Dong","raw_affiliation_strings":["Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016153320","display_name":"Kaixiang Peng","orcid":"https://orcid.org/0000-0001-8314-3047"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaixiang Peng","raw_affiliation_strings":["Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011763643","display_name":"Silvio Simani","orcid":"https://orcid.org/0000-0003-1815-2478"},"institutions":[{"id":"https://openalex.org/I201324441","display_name":"University of Ferrara","ror":"https://ror.org/041zkgm14","country_code":"IT","type":"education","lineage":["https://openalex.org/I201324441"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Silvio Simani","raw_affiliation_strings":["Department of Engineering, University of Ferrara, Ferrara, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Engineering, University of Ferrara, Ferrara, Italy","institution_ids":["https://openalex.org/I201324441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055369435","display_name":"Daye Li","orcid":"https://orcid.org/0000-0002-1043-0245"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daye Li","raw_affiliation_strings":["Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052051783","display_name":"Junming Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianing Hou","raw_affiliation_strings":["Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100570942"],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":null,"apc_paid":null,"fwci":1.2158,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85008768,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"56","issue":"4","first_page":"2009","last_page":"2020"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9872999787330627,"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":0.9872999787330627,"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":0.0017999999690800905,"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/T10791","display_name":"Advanced Control Systems Optimization","score":0.0010999999940395355,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5939000248908997},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4779999852180481},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.46209999918937683},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.4546999931335449},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.3946000039577484},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.392300009727478},{"id":"https://openalex.org/keywords/transfer-entropy","display_name":"Transfer entropy","score":0.36570000648498535},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.32109999656677246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.625},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5939000248908997},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5419999957084656},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4779999852180481},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.46209999918937683},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.4546999931335449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42910000681877136},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3939000070095062},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C182049051","wikidata":"https://www.wikidata.org/wiki/Q17147155","display_name":"Transfer entropy","level":3,"score":0.36570000648498535},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C84945661","wikidata":"https://www.wikidata.org/wiki/Q7366567","display_name":"Root cause","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C155386361","wikidata":"https://www.wikidata.org/wiki/Q1649571","display_name":"Process control","level":3,"score":0.2831000089645386},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tcyb.2025.3634611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcyb.2025.3634611","pdf_url":null,"source":{"id":"https://openalex.org/S4210191041","display_name":"IEEE Transactions on Cybernetics","issn_l":"2168-2267","issn":["2168-2267","2168-2275"],"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 Cybernetics","raw_type":"journal-article"},{"id":"pmid:41289112","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41289112","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on cybernetics","raw_type":null},{"id":"pmh:oai:sfera.unife.it:11392/2607825","is_oa":false,"landing_page_url":"https://ieeexplore.ieee.org/document/11267517","pdf_url":null,"source":{"id":"https://openalex.org/S4306400369","display_name":"Institutional Research Information System University of Ferrara (University of Ferrara)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I201324441","host_organization_name":"University of Ferrara","host_organization_lineage":["https://openalex.org/I201324441"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"large-scale":[1,43],"industrial":[2,44],"processes":[3],"evolve":[4],"toward":[5],"greater":[6],"complexity,":[7],"the":[8,83,88,127,132,165,171],"increasing":[9],"interdependence":[10],"of":[11,90,156,170,196],"networked":[12],"and":[13,59,106,124,167,181],"dynamic":[14,60,71,92,97,134],"process":[15,100,189],"data":[16,183],"has":[17],"a":[18,70,110,137,147,185,192],"critical":[19],"impact":[20],"on":[21],"product":[22],"quality,":[23],"creating":[24],"significant":[25],"challenges":[26],"for":[27],"quality-related":[28,139],"fault":[29,140,157,193],"diagnosis.":[30],"Causal":[31],"graphs":[32],"(CGs)":[33],"are":[34,51,174],"effective":[35],"in":[36,42,53,79,131],"modeling":[37],"structural":[38],"relationships":[39],"among":[40,99],"nodes":[41],"processes.":[45],"However,":[46],"traditional":[47],"causal":[48,61,72,149],"discovery":[49],"methods":[50],"limited":[52],"their":[54],"ability":[55],"to":[56,119],"represent":[57,96],"hierarchical":[58,91,133,162],"structures":[62],"with":[63,146],"spatiotemporal":[64,128],"features.":[65],"To":[66],"overcome":[67],"these":[68],"limitations,":[69],"entropy":[73],"(DCE)-spatiotemporal":[74],"convolutional":[75,114],"network":[76,116],"is":[77,117,143],"designed":[78,118],"this":[80],"article.":[81],"First,":[82],"proposed":[84,172],"DCE":[85],"method":[86,173],"enables":[87],"construction":[89],"CGs":[93],"that":[94,152],"accurately":[95],"interactions":[98],"variables,":[101],"effectively":[102],"mitigating":[103],"confounding":[104],"factors":[105],"enhancing":[107],"interpretability.":[108],"Second,":[109],"3-D":[111],"squeeze-and-excitation":[112],"(SE)":[113],"neural":[115],"adaptively":[120],"recalibrate":[121],"channel-wise":[122],"information":[123],"deeply":[125],"analyze":[126],"characteristics":[129],"embedded":[130],"CGs.":[135],"Furthermore,":[136],"local-global":[138],"detection":[141,194],"approach":[142],"introduced,":[144],"along":[145],"novel":[148],"anomaly":[150],"vector":[151],"facilitates":[153],"precise":[154],"recognition":[155],"root":[158],"causes":[159],"across":[160],"multiple":[161],"levels.":[163],"Finally,":[164],"effectiveness":[166],"practical":[168],"advantages":[169],"thoroughly":[175],"demonstrated":[176],"using":[177],"both":[178],"numerical":[179],"simulations":[180],"real-world":[182],"from":[184],"hot":[186],"strip":[187],"mill":[188],"(HSMP),":[190],"achieving":[191],"accuracy":[195],"95.78%.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-11-25T00:00:00"}
