{"id":"https://openalex.org/W4385299105","doi":"https://doi.org/10.1145/3603781.3603810","title":"Decoupling Transformer with Convolutional Fusion for Mechanical Composite Fault Diagnosis","display_name":"Decoupling Transformer with Convolutional Fusion for Mechanical Composite Fault Diagnosis","publication_year":2023,"publication_date":"2023-05-26","ids":{"openalex":"https://openalex.org/W4385299105","doi":"https://doi.org/10.1145/3603781.3603810"},"language":"en","primary_location":{"id":"doi:10.1145/3603781.3603810","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3603781.3603810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things","raw_type":"proceedings-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/A5101504747","display_name":"Xia Liu","orcid":"https://orcid.org/0000-0002-7880-2084"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xia Liu","raw_affiliation_strings":["Department of Information Science and Technology, Beijing University of Chemical Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-7880-2084","affiliations":[{"raw_affiliation_string":"Department of Information Science and Technology, Beijing University of Chemical Technology, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069512064","display_name":"Kun Feng","orcid":"https://orcid.org/0000-0002-2214-2216"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Feng","raw_affiliation_strings":["Department of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, China"],"raw_orcid":"https://orcid.org/0000-0002-2214-2216","affiliations":[{"raw_affiliation_string":"Department of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, China","institution_ids":["https://openalex.org/I75390827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101504747"],"corresponding_institution_ids":["https://openalex.org/I75390827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09694638,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"170","last_page":"179"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9932000041007996,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9932000041007996,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9887999892234802,"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":0.9670000076293945,"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/computer-science","display_name":"Computer science","score":0.7143760919570923},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6558794975280762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5398406982421875},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5040885210037231},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5001261234283447},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.48719972372055054},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4757934808731079},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4306418299674988},{"id":"https://openalex.org/keywords/decoupling","display_name":"Decoupling (probability)","score":0.4110538363456726},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34673482179641724},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16173121333122253},{"id":"https://openalex.org/keywords/control-engineering","display_name":"Control engineering","score":0.15907737612724304}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7143760919570923},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6558794975280762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5398406982421875},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5040885210037231},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5001261234283447},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.48719972372055054},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4757934808731079},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4306418299674988},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.4110538363456726},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34673482179641724},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16173121333122253},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.15907737612724304},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3603781.3603810","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3603781.3603810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2133564696","https://openalex.org/W2147800946","https://openalex.org/W2277064738","https://openalex.org/W2585519949","https://openalex.org/W2905166565","https://openalex.org/W2991661665","https://openalex.org/W3039055627","https://openalex.org/W3090238656","https://openalex.org/W3090982778","https://openalex.org/W3094502228","https://openalex.org/W3101245099","https://openalex.org/W3106901053","https://openalex.org/W3138516171","https://openalex.org/W3171087525","https://openalex.org/W3171346367","https://openalex.org/W4200049650","https://openalex.org/W4200086463","https://openalex.org/W4385245566","https://openalex.org/W6739901393","https://openalex.org/W6791943378","https://openalex.org/W6796246584"],"related_works":["https://openalex.org/W4313561376","https://openalex.org/W3103825105","https://openalex.org/W2081900870","https://openalex.org/W3027880158","https://openalex.org/W1617565119","https://openalex.org/W160381218","https://openalex.org/W2512958550","https://openalex.org/W2004102934","https://openalex.org/W4366455350","https://openalex.org/W2329266651"],"abstract_inverted_index":{"Compound":[0],"faults":[1],"often":[2],"occur":[3],"in":[4],"mechanical":[5,21],"systems":[6],"under":[7],"complex":[8],"and":[9,45,56,128,151],"variable":[10],"operating":[11],"conditions,":[12],"which":[13,107,134],"can":[14,40],"seriously":[15],"affect":[16],"the":[17,29,43,77,82,86,98,101,112,125,129,137,147,163,170],"health":[18],"level":[19],"of":[20,32,47,166],"systems,":[22],"so":[23],"it":[24],"is":[25,108],"crucial":[26],"to":[27],"study":[28],"decoupling":[30,68],"diagnosis":[31,49,65],"compound":[33,119],"faults.":[34],"In":[35,161],"addition,":[36,162],"industrial":[37],"big":[38],"data":[39,54,89],"greatly":[41],"boost":[42],"accuracy":[44],"reliability":[46],"fault":[48,64,120],"results.":[50],"Therefore,":[51],"based":[52,103],"on":[53,104,133,146],"fusion":[55,72],"attention":[57,167],"mechanism,":[58],"we":[59,75],"propose":[60],"a":[61,94],"new":[62],"composite":[63],"method":[66,139],"called":[67],"Transformer":[69],"with":[70],"convolutional":[71,95],"(DTCF).":[73],"First,":[74],"construct":[76],"input":[78],"embedding":[79],"sequence":[80],"as":[81],"model":[83,171],"input.":[84],"Second,":[85],"multichannel":[87],"sensor":[88],"are":[90,122],"adaptively":[91],"fused":[92],"using":[93],"layer.":[96],"Then,":[97],"encoder":[99],"encodes":[100],"signal":[102],"global":[105],"self-attention,":[106],"representation":[109],"learning.":[110],"Finally,":[111],"decoupler":[113],"iteratively":[114],"generates":[115],"decoupled":[116],"labels.":[117],"Two":[118],"datasets":[121],"used,":[123],"including":[124],"gearbox":[126],"dataset":[127],"bearing":[130],"dataset,":[131,150],"experiments":[132],"show":[135],"that":[136],"proposed":[138],"has":[140],"higher":[141],"accuracy,":[142],"better":[143],"generalization":[144],"ability":[145],"smaller":[148],"training":[149],"stronger":[152],"robustness":[153],"against":[154],"noise":[155],"than":[156],"CNN-based":[157],"or":[158],"MLP-based":[159],"models.":[160],"visual":[164],"analysis":[165],"weights":[168],"makes":[169],"interpretable.":[172]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}