{"id":"https://openalex.org/W3157688629","doi":"https://doi.org/10.1109/tim.2021.3077673","title":"Multiscale Convolutional Neural Network With Feature Alignment for Bearing Fault Diagnosis","display_name":"Multiscale Convolutional Neural Network With Feature Alignment for Bearing Fault Diagnosis","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3157688629","doi":"https://doi.org/10.1109/tim.2021.3077673","mag":"3157688629"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2021.3077673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2021.3077673","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/A5082242637","display_name":"Junbin Chen","orcid":"https://orcid.org/0000-0001-6345-5246"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junbin Chen","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082065343","display_name":"Ruyi Huang","orcid":"https://orcid.org/0000-0003-0586-1195"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruyi Huang","raw_affiliation_strings":["School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006293645","display_name":"Kun Zhao","orcid":"https://orcid.org/0000-0001-5487-6532"},"institutions":[{"id":"https://openalex.org/I4210093205","display_name":"Foxconn (China)","ror":"https://ror.org/00jb92367","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210093205","https://openalex.org/I4210108919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Zhao","raw_affiliation_strings":["Foxconn Industrial Internet Company, Ltd., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Foxconn Industrial Internet Company, Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210093205"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004847387","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0003-1531-5323"},"institutions":[{"id":"https://openalex.org/I4210093205","display_name":"Foxconn (China)","ror":"https://ror.org/00jb92367","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210093205","https://openalex.org/I4210108919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Foxconn Industrial Internet Company, Ltd., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Foxconn Industrial Internet Company, Ltd., Shenzhen, China","institution_ids":["https://openalex.org/I4210093205"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037969019","display_name":"Longcan Liu","orcid":"https://orcid.org/0000-0002-1798-9017"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Longcan Liu","raw_affiliation_strings":["Guangdong Artificial Intelligence and Digital Economy Laboratory, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Artificial Intelligence and Digital Economy Laboratory, Guangzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010684849","display_name":"Weihua Li","orcid":"https://orcid.org/0000-0002-7493-1399"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihua Li","raw_affiliation_strings":["Shien-Ming Wu School of Intelligent Engineering and the School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Shien-Ming Wu School of Intelligent Engineering and the School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5082242637"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":9.9833,"has_fulltext":false,"cited_by_count":96,"citation_normalized_percentile":{"value":0.98728057,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"70","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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":0.9904999732971191,"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":0.9593999981880188,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7951390743255615},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7085832953453064},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6949710249900818},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6899400353431702},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.679257869720459},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6223053336143494},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5713882446289062},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5100720524787903},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5000104904174805},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48490720987319946},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4104248583316803},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33405715227127075}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7951390743255615},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7085832953453064},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6949710249900818},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6899400353431702},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.679257869720459},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6223053336143494},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5713882446289062},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5100720524787903},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5000104904174805},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48490720987319946},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4104248583316803},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33405715227127075},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2021.3077673","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2021.3077673","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":[],"awards":[{"id":"https://openalex.org/G164778973","display_name":null,"funder_award_id":"2018YFB1702400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6095112105","display_name":null,"funder_award_id":"51875208","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"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W2060304859","https://openalex.org/W2327501747","https://openalex.org/W2584994008","https://openalex.org/W2603304445","https://openalex.org/W2767346351","https://openalex.org/W2783074568","https://openalex.org/W2794869810","https://openalex.org/W2807007689","https://openalex.org/W2810292802","https://openalex.org/W2905166565","https://openalex.org/W2906256948","https://openalex.org/W2942682738","https://openalex.org/W2946048316","https://openalex.org/W2947583263","https://openalex.org/W2954996726","https://openalex.org/W2956467153","https://openalex.org/W2958041981","https://openalex.org/W2968409655","https://openalex.org/W2972034512","https://openalex.org/W2991632793","https://openalex.org/W2991661665","https://openalex.org/W2995279030","https://openalex.org/W2996904338","https://openalex.org/W2998780022","https://openalex.org/W2998830408","https://openalex.org/W3004451154","https://openalex.org/W3008309516","https://openalex.org/W3021496598","https://openalex.org/W3026006566","https://openalex.org/W3033236487","https://openalex.org/W3044967028","https://openalex.org/W3049488605","https://openalex.org/W3060850527","https://openalex.org/W3089183752","https://openalex.org/W3095982099","https://openalex.org/W3106901053","https://openalex.org/W3107076389","https://openalex.org/W6618372016","https://openalex.org/W6638444622","https://openalex.org/W6751831877"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W4401096132","https://openalex.org/W2964954556"],"abstract_inverted_index":{"In":[0],"recent":[1,21],"years,":[2],"deep":[3],"learning":[4],"methods,":[5],"especially":[6],"convolutional":[7,90],"neural":[8],"network":[9],"(CNN),":[10],"have":[11,23],"received":[12],"extensive":[13],"attentions":[14],"and":[15,39,92,106,132,179],"applications":[16],"in":[17,35,42,49,174],"fault":[18,76,153],"diagnosis.":[19,154],"However,":[20],"studies":[22],"shown":[24],"that":[25],"the":[26,46,50,54,59,85,89,158],"shift-invariance":[27,55],"of":[28,56,62,88,144,157,176],"CNN":[29,67],"is":[30,72,111,130,138,149,160],"not":[31],"good":[32],"enough,":[33],"resulting":[34],"fragile":[36],"feature":[37,69,96,136,180],"extraction":[38],"sharp":[40],"reduction":[41],"model":[43],"performance":[44],"when":[45],"shift":[47],"occurs":[48],"input.":[51],"To":[52],"improve":[53],"CNN,":[57],"considering":[58],"periodic":[60],"characteristics":[61],"vibration":[63,124],"signals,":[64,125],"a":[65,95,126,133,141,163],"multiscale":[66,127,135],"with":[68],"alignment":[70,97],"(MSCNN-FA)":[71],"proposed":[73],"for":[74,151],"bearing":[75,152,165],"diagnosis":[77,177],"under":[78],"different":[79],"working":[80],"conditions.":[81],"First,":[82],"by":[83,162],"analyzing":[84],"operating":[86],"principles":[87],"layer":[91,110],"pooling":[93,109],"layer,":[94,102,105],"module":[98],"including":[99],"single-stride":[100],"convolution":[101,128],"adaptive":[103],"max-pooling":[104],"global":[107],"average":[108],"designed":[112],"to":[113,118],"obtain":[114],"aligned":[115],"features.":[116],"Next,":[117],"extract":[119],"shift-invariant":[120],"robust":[121],"features":[122],"from":[123],"strategy":[129],"utilized,":[131],"feature-aligned":[134],"extractor":[137],"constructed.":[139],"Finally,":[140],"classifier":[142],"composed":[143],"fully":[145],"connected":[146],"(FC)":[147],"layers":[148],"constructed":[150],"The":[155],"effectiveness":[156],"method":[159],"verified":[161],"rolling":[164],"experiment,":[166],"which":[167],"outperforms":[168],"other":[169],"related":[170],"existing":[171],"CNN-based":[172],"methods":[173],"terms":[175],"accuracy":[178],"robustness.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":26},{"year":2023,"cited_by_count":24},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}