{"id":"https://openalex.org/W7225828168","doi":"https://doi.org/10.1088/2631-8695/aec101","title":"A Physics-Inspired CNN-Transformer Synergistic Framework for Bearing Fault Diagnosis under Strong Noise Environments","display_name":"A Physics-Inspired CNN-Transformer Synergistic Framework for Bearing Fault Diagnosis under Strong Noise Environments","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225828168","doi":"https://doi.org/10.1088/2631-8695/aec101"},"language":"en","primary_location":{"id":"doi:10.1088/2631-8695/aec101","doi":"https://doi.org/10.1088/2631-8695/aec101","is_oa":false,"landing_page_url":"https://doi.org/10.1088/2631-8695/aec101","pdf_url":null,"source":{"id":"https://openalex.org/S4210221144","display_name":"Engineering Research Express","issn_l":"2631-8695","issn":["2631-8695"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-20","russia-white-list-3"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Research Express","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"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/A5060760259","display_name":"Chuang Liang","orcid":"https://orcid.org/0000-0001-7132-2590","observed_orcids":["https://orcid.org/0000-0001-7132-2590"]},"institutions":[{"id":"https://openalex.org/I116036724","display_name":"Shenyang Ligong University","ror":"https://ror.org/03m20nr07","country_code":"CN","type":"education","lineage":["https://openalex.org/I116036724"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuang Liang","raw_affiliation_strings":["Shenyang Ligong University"],"raw_orcid":"https://orcid.org/0009-0005-1328-561X","affiliations":[{"raw_affiliation_string":"Shenyang Ligong University","institution_ids":["https://openalex.org/I116036724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100873575","display_name":"Zhen Jiang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I116036724","display_name":"Shenyang Ligong University","ror":"https://ror.org/03m20nr07","country_code":"CN","type":"education","lineage":["https://openalex.org/I116036724"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Jiang","raw_affiliation_strings":["Shenyang Ligong University"],"raw_orcid":"https://orcid.org/0009-0009-9431-1830","affiliations":[{"raw_affiliation_string":"Shenyang Ligong University","institution_ids":["https://openalex.org/I116036724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081137057","display_name":"Yonghe Wei","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I116036724","display_name":"Shenyang Ligong University","ror":"https://ror.org/03m20nr07","country_code":"CN","type":"education","lineage":["https://openalex.org/I116036724"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"yonghe wei","raw_affiliation_strings":["Shenyang Ligong University"],"raw_orcid":"https://orcid.org/0009-0006-1733-5001","affiliations":[{"raw_affiliation_string":"Shenyang Ligong University","institution_ids":["https://openalex.org/I116036724"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100408504","display_name":"Xiao-Guang Zhang","orcid":"https://orcid.org/0000-0002-3298-7730","observed_orcids":["https://orcid.org/0000-0002-3298-7730"]},"institutions":[{"id":"https://openalex.org/I4405282086","display_name":"Fuyao University of Science and Technology","ror":"https://ror.org/05aak3d90","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405282086"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao-Guang Zhang","raw_affiliation_strings":["Fuyao University of Science and Technology"],"raw_orcid":"https://orcid.org/0000-0002-3298-7730","affiliations":[{"raw_affiliation_string":"Fuyao University of Science and Technology","institution_ids":["https://openalex.org/I4405282086"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5161451244","display_name":"Chengcheng Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405280630","display_name":"Shenyang Institute of Science and Technology","ror":"https://ror.org/037670g05","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405280630"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengcheng Wang","raw_affiliation_strings":["Shenyang Institute of Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenyang Institute of Science and Technology","institution_ids":["https://openalex.org/I4405280630"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2400,"currency":"USD","value_usd":2400},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","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/T10220","display_name":"Machine Fault Diagnosis Techniques","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/T10876","display_name":"Fault Detection and Control Systems","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/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.0,"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"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7135000228881836},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6705999970436096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6126000285148621},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5203999876976013},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.499099999666214},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.448199987411499},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.4388999938964844},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.3849000036716461},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.3544999957084656},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.3458999991416931},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34200000762939453},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3296000063419342},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.32519999146461487},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.31200000643730164},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.29989999532699585},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.2808000147342682},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.26969999074935913},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.26919999718666077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1088/2631-8695/aec101","doi":"https://doi.org/10.1088/2631-8695/aec101","is_oa":false,"landing_page_url":"https://doi.org/10.1088/2631-8695/aec101","pdf_url":null,"source":{"id":"https://openalex.org/S4210221144","display_name":"Engineering Research Express","issn_l":"2631-8695","issn":["2631-8695"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","norway-1","poland-20","russia-white-list-3"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Research Express","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Rolling":[1],"bearings":[2],"play":[3],"a":[4,76,126,174],"crucial":[5],"role":[6],"in":[7,43,57],"the":[8,18,24,84,107,147,166,198,203,211,214,244],"operation":[9,20],"of":[10,21,26,48,88,109,202,213],"rotating":[11,49],"machinery":[12],"within":[13],"industrial":[14],"systems.":[15],"To":[16,114],"ensure":[17],"stable":[19],"mechanical":[22],"equipment,":[23],"accuracy":[25],"bearing":[27,143,231],"fault":[28,46,100,111,144,220],"diagnosis":[29,47,221],"is":[30,184],"particularly":[31],"critical.":[32],"In":[33],"recent":[34],"years,":[35],"deep":[36],"learning":[37,178],"(DL)":[38],"has":[39],"been":[40,55],"widely":[41],"applied":[42],"research":[44],"on":[45,229],"machinery.":[50],"Although":[51],"significant":[52],"achievements":[53],"have":[54],"made":[56],"this":[58,118],"field,":[59],"some":[60],"challenges":[61],"still":[62],"persist.":[63],"Traditional":[64],"convolutional":[65,215],"neural":[66],"networks":[67],"(CNNs),":[68],"while":[69],"adept":[70],"at":[71],"extracting":[72],"local":[73,167],"textures,":[74],"lack":[75],"global":[77,140,207],"perspective":[78],"and":[79,94,135,139,217,225,250],"struggle":[80],"to":[81,99,106,133,164,193],"fully":[82],"characterize":[83],"overall":[85],"health":[86],"status":[87],"bearings.":[89],"Moreover,":[90],"under":[91,253],"strong":[92,254],"noise":[93,255],"coupled":[95],"interference,":[96],"their":[97],"sensitivity":[98],"features":[101],"can":[102],"be":[103],"limited,":[104],"leading":[105],"loss":[108],"key":[110],"characteristic":[112],"information.":[113],"address":[115],"these":[116],"issues,":[117],"paper":[119],"proposes":[120],"Efficient":[121],"Multi-scale":[122,176],"Convolutional-Transformer":[123],"Network":[124],"(EMCT-Net),":[125],"physics-":[127],"inspired":[128],"CNN-Transformer":[129],"synergistic":[130],"framework":[131],"designed":[132,175],"decouple":[134],"extract":[136],"transient":[137,168],"impacts":[138],"periodicities":[141],"from":[142,233],"signals.":[145],"First,":[146],"acquired":[148],"raw":[149],"one-dimensional":[150],"vibration":[151],"signals":[152],"are":[153,227],"converted":[154],"into":[155],"two-dimensional":[156],"time-frequency":[157],"images":[158],"using":[159],"continuous":[160],"wavelet":[161],"transform.":[162],"Subsequently,":[163],"capture":[165],"energy":[169],"bursts":[170],"characterizing":[171],"early":[172],"faults,":[173],"Cross-spatial":[177],"Convolutional":[179],"Attention":[180],"(MCCA)":[181],"fusion":[182],"module":[183],"employed.":[185],"The":[186],"MCCA":[187],"acts":[188],"as":[189],"an":[190],"information":[191],"bottleneck":[192],"restrict":[194],"unconstrained":[195],"noise.":[196],"Finally,":[197],"sliding":[199],"window":[200],"mechanism":[201],"Swin":[204],"Transformer":[205],"captures":[206],"information,":[208],"compensating":[209],"for":[210],"limitations":[212],"module,":[216],"ultimately":[218],"achieves":[219],"classification.":[222],"EMCT-Net's":[223],"superiority":[224],"robustness":[226,249],"validated":[228],"three":[230],"datasets":[232],"different":[234],"perspectives.":[235],"Comparative":[236],"evaluations":[237],"with":[238],"various":[239],"state-of-the-art":[240],"methods":[241],"demonstrate":[242],"that":[243],"proposed":[245],"approach":[246],"exhibits":[247],"superior":[248],"accuracy,":[251],"even":[252],"interference.":[256]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
