{"id":"https://openalex.org/W4206726927","doi":"https://doi.org/10.1109/tai.2021.3134186","title":"Feature-Aligned Stacked Autoencoder: A Novel Semisupervised Deep Learning Model for Pattern Classification of Industrial Faults","display_name":"Feature-Aligned Stacked Autoencoder: A Novel Semisupervised Deep Learning Model for Pattern Classification of Industrial Faults","publication_year":2021,"publication_date":"2021-12-13","ids":{"openalex":"https://openalex.org/W4206726927","doi":"https://doi.org/10.1109/tai.2021.3134186"},"language":"en","primary_location":{"id":"doi:10.1109/tai.2021.3134186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2021.3134186","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","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/A5100654679","display_name":"Xinmin Zhang","orcid":"https://orcid.org/0000-0002-4761-3969"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinmin Zhang","raw_affiliation_strings":["State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-4761-3969","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120698579","display_name":"Hongyi Zhang","orcid":"https://orcid.org/0009-0006-8179-3162"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyi Zhang","raw_affiliation_strings":["State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048733958","display_name":"Zhihuan Song","orcid":"https://orcid.org/0000-0003-4098-6479"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihuan Song","raw_affiliation_strings":["State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4098-6479","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I4391767838"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4391767838"],"apc_list":null,"apc_paid":null,"fwci":2.4944,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.89344557,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"4","issue":"4","first_page":"592","last_page":"601"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9952999949455261,"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"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9896000027656555,"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/autoencoder","display_name":"Autoencoder","score":0.961880087852478},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.9108034372329712},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7651933431625366},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6823866963386536},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6555836796760559},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6375241279602051},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6304994225502014},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5865459442138672},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5848545432090759},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.48755472898483276},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.48225241899490356},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.43881386518478394},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4062543213367462},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33408963680267334},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16174349188804626}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.961880087852478},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9108034372329712},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7651933431625366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6823866963386536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6555836796760559},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6375241279602051},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6304994225502014},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5865459442138672},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5848545432090759},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.48755472898483276},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.48225241899490356},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.43881386518478394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4062543213367462},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33408963680267334},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16174349188804626},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tai.2021.3134186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tai.2021.3134186","pdf_url":null,"source":{"id":"https://openalex.org/S4210169448","display_name":"IEEE Transactions on Artificial Intelligence","issn_l":"2691-4581","issn":["2691-4581"],"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 Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5699999928474426,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1987348790","display_name":null,"funder_award_id":"61833014","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1988872666","display_name":null,"funder_award_id":"62003301","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":45,"referenced_works":["https://openalex.org/W385466589","https://openalex.org/W1507417347","https://openalex.org/W1970537494","https://openalex.org/W1985475221","https://openalex.org/W1991519797","https://openalex.org/W1994616650","https://openalex.org/W2004186751","https://openalex.org/W2010001505","https://openalex.org/W2031423206","https://openalex.org/W2076063813","https://openalex.org/W2079639858","https://openalex.org/W2100495367","https://openalex.org/W2110798204","https://openalex.org/W2128728535","https://openalex.org/W2158131535","https://openalex.org/W2158958729","https://openalex.org/W2324044936","https://openalex.org/W2463280635","https://openalex.org/W2480364715","https://openalex.org/W2530806392","https://openalex.org/W2741800562","https://openalex.org/W2757109865","https://openalex.org/W2764120290","https://openalex.org/W2767669147","https://openalex.org/W2788805965","https://openalex.org/W2793534717","https://openalex.org/W2885282028","https://openalex.org/W2892709813","https://openalex.org/W2901643192","https://openalex.org/W2944138212","https://openalex.org/W2959724594","https://openalex.org/W2966742700","https://openalex.org/W2967723820","https://openalex.org/W2971407654","https://openalex.org/W2972189551","https://openalex.org/W2974000087","https://openalex.org/W2989600764","https://openalex.org/W2997574889","https://openalex.org/W3018109943","https://openalex.org/W3025505824","https://openalex.org/W3126562619","https://openalex.org/W3160417254","https://openalex.org/W4248342355","https://openalex.org/W6681096077","https://openalex.org/W6682962330"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W2810330923","https://openalex.org/W4289763776","https://openalex.org/W2786764570","https://openalex.org/W4309960894","https://openalex.org/W4312364074"],"abstract_inverted_index":{"Autoencoder":[0],"is":[1,44,90,125],"a":[2,51,85],"widely":[3],"used":[4],"deep":[5,54],"learning":[6,55],"method,":[7],"which":[8],"first":[9],"extracts":[10],"features":[11,103],"from":[12,105],"all":[13],"data":[14,34,68,109],"through":[15,110,127],"unsupervised":[16],"reconstruction,":[17],"and":[18,43,80,107,134,147],"then":[19],"fine-tunes":[20],"the":[21,29,36,66,70,75,94,99,102,111,115,122,135,140,155],"network":[22,37,113],"with":[23],"labeled":[24,33,79,106],"data.":[25,82],"However,":[26],"due":[27],"to":[28,46,130,154],"limited":[30],"number":[31],"of":[32,65,77,98,121],"samples,":[35],"may":[38],"lack":[39],"sufficient":[40],"generalization":[41,145],"ability":[42,146],"prone":[45],"overfitting.":[47],"This":[48],"article":[49],"proposes":[50],"new":[52,86],"semisupervised":[53],"method":[56],"called":[57],"feature-aligned":[58],"stacked":[59],"autoencoder":[60],"(FA-SAE).":[61],"FA-SAE":[62,124,142],"takes":[63],"advantage":[64],"unlabeled":[67,81,108],"during":[69],"fine-tuning":[71],"process":[72],"by":[73,92],"aligning":[74],"feature":[76],"both":[78],"In":[83],"FA-SAE,":[84],"training":[87],"loss":[88],"function":[89],"designed":[91],"integrating":[93],"Sinkhorn":[95],"distance":[96],"measure":[97],"difference":[100],"between":[101],"extracted":[104],"neural":[112],"into":[114],"cross-entropy":[116],"classification":[117,150],"loss.":[118],"The":[119],"effectiveness":[120],"proposed":[123,141],"verified":[126],"its":[128],"application":[129,136],"two":[131],"industrial":[132],"processes,":[133],"results":[137],"demonstrated":[138],"that":[139],"has":[143],"better":[144],"higher":[148],"fault":[149],"accuracy":[151],"as":[152],"compared":[153],"state-of-the-art":[156],"methods.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}