{"id":"https://openalex.org/W3182724413","doi":"https://doi.org/10.1109/icps49255.2021.9468130","title":"A Deep Residual Convolutional Neural Network based Bearing Fault Diagnosis with Multi-Sensor Data","display_name":"A Deep Residual Convolutional Neural Network based Bearing Fault Diagnosis with Multi-Sensor Data","publication_year":2021,"publication_date":"2021-05-10","ids":{"openalex":"https://openalex.org/W3182724413","doi":"https://doi.org/10.1109/icps49255.2021.9468130","mag":"3182724413"},"language":"en","primary_location":{"id":"doi:10.1109/icps49255.2021.9468130","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icps49255.2021.9468130","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)","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/A5077784565","display_name":"Guangxing Niu","orcid":"https://orcid.org/0000-0002-1589-7055"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guangxing Niu","raw_affiliation_strings":["University of South Carolina, Columbia, SC"],"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013314595","display_name":"Enhui Liu","orcid":"https://orcid.org/0000-0003-1684-5369"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Enhui Liu","raw_affiliation_strings":["University of South Carolina, Columbia, SC"],"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100392843","display_name":"Bin Zhang","orcid":"https://orcid.org/0000-0002-4879-0211"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Zhang","raw_affiliation_strings":["University of South Carolina, Columbia, SC"],"affiliations":[{"raw_affiliation_string":"University of South Carolina, Columbia, SC","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064448867","display_name":"Michael Golda","orcid":null},"institutions":[{"id":"https://openalex.org/I2802287952","display_name":"Naval Surface Warfare Center","ror":"https://ror.org/03d4ecn10","country_code":"US","type":"facility","lineage":["https://openalex.org/I1328969757","https://openalex.org/I1330347796","https://openalex.org/I2802287952","https://openalex.org/I3130687028"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Golda","raw_affiliation_strings":["Philadelphia Division (NSWCPD), Naval Surface Warfare Center, Philadelphia, PA"],"affiliations":[{"raw_affiliation_string":"Philadelphia Division (NSWCPD), Naval Surface Warfare Center, Philadelphia, PA","institution_ids":["https://openalex.org/I2802287952"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068133076","display_name":"Stephen A. Mastro","orcid":null},"institutions":[{"id":"https://openalex.org/I2802287952","display_name":"Naval Surface Warfare Center","ror":"https://ror.org/03d4ecn10","country_code":"US","type":"facility","lineage":["https://openalex.org/I1328969757","https://openalex.org/I1330347796","https://openalex.org/I2802287952","https://openalex.org/I3130687028"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stephen Mastro","raw_affiliation_strings":["Philadelphia Division (NSWCPD), Naval Surface Warfare Center, Philadelphia, PA"],"affiliations":[{"raw_affiliation_string":"Philadelphia Division (NSWCPD), Naval Surface Warfare Center, Philadelphia, PA","institution_ids":["https://openalex.org/I2802287952"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5077784565"],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":0.412,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6025148,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"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":0.9987000226974487,"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":0.9987000226974487,"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":0.9929999709129333,"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/T13213","display_name":"Mechanical Failure Analysis and Simulation","score":0.9514999985694885,"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":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8153148293495178},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7019172310829163},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.67828369140625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6605546474456787},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6521944403648376},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6513502597808838},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6186652183532715},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5892481207847595},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5494149327278137},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5209485292434692},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.518288791179657},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.44936928153038025},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33220165967941284},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22322869300842285},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.13017088174819946},{"id":"https://openalex.org/keywords/electronic-engineering","display_name":"Electronic engineering","score":0.08457106351852417}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8153148293495178},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7019172310829163},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.67828369140625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6605546474456787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6521944403648376},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6513502597808838},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6186652183532715},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5892481207847595},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5494149327278137},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5209485292434692},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.518288791179657},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.44936928153038025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33220165967941284},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22322869300842285},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13017088174819946},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.08457106351852417},{"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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icps49255.2021.9468130","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icps49255.2021.9468130","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)","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":18,"referenced_works":["https://openalex.org/W1932847118","https://openalex.org/W2194775991","https://openalex.org/W2230524333","https://openalex.org/W2692693673","https://openalex.org/W2768753204","https://openalex.org/W2791694051","https://openalex.org/W2892748417","https://openalex.org/W2910957091","https://openalex.org/W2912412749","https://openalex.org/W2939978363","https://openalex.org/W2973424371","https://openalex.org/W2975932043","https://openalex.org/W3008309516","https://openalex.org/W3136556279","https://openalex.org/W4214632657","https://openalex.org/W6754781324","https://openalex.org/W6791725246","https://openalex.org/W6808868985"],"related_works":["https://openalex.org/W4299822940","https://openalex.org/W2279398222","https://openalex.org/W2773120646","https://openalex.org/W3011074480","https://openalex.org/W2059299633","https://openalex.org/W2738221750","https://openalex.org/W4366492315","https://openalex.org/W2940977206","https://openalex.org/W564581980","https://openalex.org/W2732542196"],"abstract_inverted_index":{"Accurate":[0],"bearing":[1,74,119,132],"fault":[2,50,75,133],"diagnosis":[3,51,113,134,184],"is":[4,61,154],"essential":[5],"and":[6,11,52,77,110,114,141,149,170,174],"significant":[7,80],"for":[8,49,63,146],"the":[9,19,107,157,178],"safety":[10],"reliability":[12],"of":[13,22,34,44,91,112,118,159],"industrial":[14],"rotating":[15,168],"systems.":[16],"To":[17,105],"monitor":[18],"health":[20],"condition":[21],"bearings":[23],"in":[24,31,73,135],"full":[25],"scales,":[26],"multi-sensors":[27],"are":[28,139,164],"always":[29],"assembled":[30],"different":[32,39,42,47,167],"locations":[33,43],"bearings.":[35],"The":[36,151],"data":[37,60,138,158],"from":[38,58],"types":[40],"or":[41],"sensors":[45],"have":[46,69,78],"sensitivity":[48],"localization,":[53],"which":[54,136,163],"makes":[55],"feature":[56,147],"extraction":[57,148],"multi-source":[59],"critical":[62],"diagnosis.":[64,150],"Deep":[65,82],"learning":[66,97],"based":[67,131],"approaches":[68],"been":[70],"widely":[71],"used":[72],"diagnosis,":[76],"achieved":[79],"successes.":[81],"residual":[83,127],"convolutional":[84,92,128],"neural":[85,93,129],"network,":[86],"as":[87],"an":[88],"improved":[89],"structure":[90],"networks,":[94],"has":[95],"powerful":[96],"ability":[98],"when":[99],"dealing":[100],"with":[101,186],"a":[102,125],"deeper":[103],"network.":[104],"improve":[106],"training":[108],"efficiency":[109],"accuracy":[111,185],"make":[115],"better":[116],"use":[117],"vibration":[120],"data,":[121],"this":[122],"paper":[123],"proposes":[124],"deep":[126],"network":[130],"multisensor":[137],"converted":[140],"combined":[142],"into":[143],"grayscale":[144],"images":[145],"proposed":[152,179],"approach":[153,180],"verified":[155],"using":[156],"tapered":[160],"roller":[161],"bearings,":[162],"tested":[165],"under":[166],"speeds":[169],"loads.":[171],"Experimental":[172],"results":[173],"comparisons":[175],"show":[176],"that":[177],"can":[181],"achieve":[182],"promising":[183],"high":[187],"efficiency.":[188]},"counts_by_year":[{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}