{"id":"https://openalex.org/W3115356548","doi":"https://doi.org/10.1109/tim.2020.3038596","title":"A New Structured Domain Adversarial Neural Network for Transfer Fault Diagnosis of Rolling Bearings Under Different Working Conditions","display_name":"A New Structured Domain Adversarial Neural Network for Transfer Fault Diagnosis of Rolling Bearings Under Different Working Conditions","publication_year":2020,"publication_date":"2020-12-28","ids":{"openalex":"https://openalex.org/W3115356548","doi":"https://doi.org/10.1109/tim.2020.3038596","mag":"3115356548"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2020.3038596","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2020.3038596","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","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/A5033586311","display_name":"Wentao Mao","orcid":"https://orcid.org/0000-0001-5335-9517"},"institutions":[{"id":"https://openalex.org/I75955062","display_name":"Henan Normal University","ror":"https://ror.org/00s13br28","country_code":"CN","type":"education","lineage":["https://openalex.org/I75955062"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wentao Mao","raw_affiliation_strings":["Engineering Lab of Intelligence Business and Internet of Things of Henan Province, Henan Normal University, Xinxiang, China","School of Computer and Information Engineering, Henan Normal University, Xinxiang, China"],"affiliations":[{"raw_affiliation_string":"Engineering Lab of Intelligence Business and Internet of Things of Henan Province, Henan Normal University, Xinxiang, China","institution_ids":["https://openalex.org/I75955062"]},{"raw_affiliation_string":"School of Computer and Information Engineering, Henan Normal University, Xinxiang, China","institution_ids":["https://openalex.org/I75955062"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100726796","display_name":"Yamin Liu","orcid":"https://orcid.org/0000-0002-1509-8368"},"institutions":[{"id":"https://openalex.org/I75955062","display_name":"Henan Normal University","ror":"https://ror.org/00s13br28","country_code":"CN","type":"education","lineage":["https://openalex.org/I75955062"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yamin Liu","raw_affiliation_strings":["School of Computer and Information Engineering, Henan Normal University, Xinxiang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan Normal University, Xinxiang, China","institution_ids":["https://openalex.org/I75955062"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101555924","display_name":"Ling Ding","orcid":"https://orcid.org/0000-0001-6579-2596"},"institutions":[{"id":"https://openalex.org/I75955062","display_name":"Henan Normal University","ror":"https://ror.org/00s13br28","country_code":"CN","type":"education","lineage":["https://openalex.org/I75955062"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Ding","raw_affiliation_strings":["School of Computer and Information Engineering, Henan Normal University, Xinxiang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan Normal University, Xinxiang, China","institution_ids":["https://openalex.org/I75955062"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048375524","display_name":"Ali Safian","orcid":"https://orcid.org/0000-0002-9361-2924"},"institutions":[{"id":"https://openalex.org/I46247651","display_name":"University of Manitoba","ror":"https://ror.org/02gfys938","country_code":"CA","type":"education","lineage":["https://openalex.org/I46247651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ali Safian","raw_affiliation_strings":["University of Manitoba, Winnipeg, Canada"],"affiliations":[{"raw_affiliation_string":"University of Manitoba, Winnipeg, Canada","institution_ids":["https://openalex.org/I46247651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078476204","display_name":"Xihui Liang","orcid":"https://orcid.org/0000-0003-1192-1238"},"institutions":[{"id":"https://openalex.org/I46247651","display_name":"University of Manitoba","ror":"https://ror.org/02gfys938","country_code":"CA","type":"education","lineage":["https://openalex.org/I46247651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xihui Liang","raw_affiliation_strings":["University of Manitoba, Winnipeg, Canada"],"affiliations":[{"raw_affiliation_string":"University of Manitoba, Winnipeg, Canada","institution_ids":["https://openalex.org/I46247651"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5033586311"],"corresponding_institution_ids":["https://openalex.org/I75955062"],"apc_list":null,"apc_paid":null,"fwci":8.458,"has_fulltext":false,"cited_by_count":111,"citation_normalized_percentile":{"value":0.98254866,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"70","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.9972000122070312,"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/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9409999847412109,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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":0.6736935377120972},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6611150503158569},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.604986846446991},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5573900938034058},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5331537127494812},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5194180011749268},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48367413878440857},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.47698765993118286},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4705265760421753},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4627103805541992},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.4531642496585846},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4222645163536072},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4126283824443817},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4114851951599121},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3631822466850281},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1838645040988922},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17042547464370728}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6736935377120972},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6611150503158569},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.604986846446991},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5573900938034058},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5331537127494812},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5194180011749268},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48367413878440857},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.47698765993118286},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4705265760421753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4627103805541992},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.4531642496585846},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4222645163536072},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4126283824443817},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4114851951599121},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3631822466850281},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1838645040988922},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17042547464370728},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"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/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2020.3038596","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2020.3038596","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7400000095367432}],"awards":[{"id":"https://openalex.org/G1902096088","display_name":null,"funder_award_id":"RGPIN-2019-05361","funder_id":"https://openalex.org/F4320334593","funder_display_name":"Natural Sciences and Engineering Research Council of Canada"},{"id":"https://openalex.org/G6017519212","display_name":null,"funder_award_id":"2020PL09","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320311952","display_name":"University of Manitoba","ror":"https://ror.org/02gfys938"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1502922572","https://openalex.org/W1731081199","https://openalex.org/W1882958252","https://openalex.org/W1903038885","https://openalex.org/W1982696459","https://openalex.org/W2004592267","https://openalex.org/W2040647667","https://openalex.org/W2081489378","https://openalex.org/W2115403315","https://openalex.org/W2125283600","https://openalex.org/W2149466042","https://openalex.org/W2159291411","https://openalex.org/W2170607218","https://openalex.org/W2255388164","https://openalex.org/W2283717164","https://openalex.org/W2464878551","https://openalex.org/W2556013418","https://openalex.org/W2589599921","https://openalex.org/W2612554669","https://openalex.org/W2744604411","https://openalex.org/W2763583057","https://openalex.org/W2768753204","https://openalex.org/W2887767982","https://openalex.org/W2903917280","https://openalex.org/W2904460913","https://openalex.org/W2907541186","https://openalex.org/W2908062660","https://openalex.org/W2929444508","https://openalex.org/W2962684181","https://openalex.org/W2963826681","https://openalex.org/W2966008650","https://openalex.org/W2969902687","https://openalex.org/W2978531559","https://openalex.org/W2983948788","https://openalex.org/W2990122260","https://openalex.org/W2995279030","https://openalex.org/W3016129047","https://openalex.org/W3024666507","https://openalex.org/W6629804754","https://openalex.org/W6637618735","https://openalex.org/W6639480849","https://openalex.org/W6640069074","https://openalex.org/W6681637710","https://openalex.org/W6683633756","https://openalex.org/W6767739360"],"related_works":["https://openalex.org/W2357124094","https://openalex.org/W2387399993","https://openalex.org/W2389739210","https://openalex.org/W2348924972","https://openalex.org/W2365736347","https://openalex.org/W2047454415","https://openalex.org/W2070040999","https://openalex.org/W2387293848","https://openalex.org/W3121791438","https://openalex.org/W2250140200"],"abstract_inverted_index":{"This":[0],"article":[1],"presents":[2],"a":[3,72,77,103,109,131],"new":[4,73,110],"deep":[5,183],"transfer":[6,58,179],"learning":[7],"method,":[8],"named":[9],"structured":[10,46],"domain":[11,40],"adversarial":[12,41],"neural":[13,42],"network":[14,43,142],"(SDANN),":[15],"for":[16,61,82],"bearing":[17,157],"fault":[18,128],"diagnosis":[19,169,180,185],"with":[20,76,112],"the":[21,36,55,65,93,122,141,164],"data":[22,66,158,174],"collected":[23,67],"under":[24],"different":[25,69],"working":[26,70],"conditions.":[27],"The":[28,160],"key":[29],"idea":[30],"of":[31,39,57,95,101,189],"this":[32,116],"method":[33,166],"is":[34,80,106,118,137],"integrating":[35],"strong":[37],"adaptability":[38],"(DANN)":[44],"and":[45,108,143,175,182,192],"relatedness":[47,104],"information":[48],"among":[49,126],"multiple":[50,127],"failure":[51],"modes":[52],"to":[53,91,120,139],"improve":[54,92],"effect":[56],"learning.":[59],"First,":[60],"fine-grained":[62],"alignment":[63],"between":[64],"from":[68],"conditions,":[71],"loss":[74],"function":[75],"discriminative":[78],"regularizer":[79,111],"designed":[81,119],"DANN":[83,96],"by":[84],"using":[85],"maximum":[86],"correlation":[87],"entropy":[88],"constraint.":[89],"Second,":[90],"stability":[94],"on":[97,115,153,171],"an":[98,145],"insufficient":[99,172],"amount":[100],"data,":[102],"matrix":[105,117],"introduced,":[107],"symmetry":[113],"constraint":[114],"capture":[121],"intrinsic":[123],"similarity":[124],"structure":[125],"types.":[129],"Finally,":[130],"stochastic":[132],"gradient":[133],"descent":[134],"optimization":[135],"strategy":[136],"used":[138,156],"train":[140],"establish":[144],"end-to-end":[146],"diagnostic":[147,190],"model.":[148],"Comparative":[149],"experiments":[150],"are":[151],"conducted":[152],"two":[154],"widely":[155],"sets.":[159],"results":[161],"show":[162],"that":[163],"proposed":[165],"has":[167],"good":[168],"performance":[170],"monitoring":[173],"outperforms":[176],"several":[177],"state-of-the-art":[178],"methods":[181,186],"learning-based":[184],"in":[187],"terms":[188],"accuracy":[191],"numerical":[193],"stability.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":12}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}