{"id":"https://openalex.org/W4206770926","doi":"https://doi.org/10.3390/s22020671","title":"A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks","display_name":"A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks","publication_year":2022,"publication_date":"2022-01-16","ids":{"openalex":"https://openalex.org/W4206770926","doi":"https://doi.org/10.3390/s22020671","pmid":"https://pubmed.ncbi.nlm.nih.gov/35062632"},"language":"en","primary_location":{"id":"doi:10.3390/s22020671","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22020671","pdf_url":"https://www.mdpi.com/1424-8220/22/2/671/pdf?version=1642475664","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/2/671/pdf?version=1642475664","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042492696","display_name":"Daoguang Yang","orcid":"https://orcid.org/0000-0001-9217-4427"},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Daoguang Yang","raw_affiliation_strings":["Department of Mechnical Engineering, Politecnico di Milano, 20156 Milan, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechnical Engineering, Politecnico di Milano, 20156 Milan, Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090855504","display_name":"Hamid Reza Karimi","orcid":null},"institutions":[{"id":"https://openalex.org/I93860229","display_name":"Politecnico di Milano","ror":"https://ror.org/01nffqt88","country_code":"IT","type":"education","lineage":["https://openalex.org/I93860229"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Hamid Reza Karimi","raw_affiliation_strings":["Department of Mechnical Engineering, Politecnico di Milano, 20156 Milan, Italy"],"raw_orcid":"https://orcid.org/0000-0001-7629-3266","affiliations":[{"raw_affiliation_string":"Department of Mechnical Engineering, Politecnico di Milano, 20156 Milan, Italy","institution_ids":["https://openalex.org/I93860229"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030663238","display_name":"Len Gelman","orcid":"https://orcid.org/0000-0001-5464-6227"},"institutions":[{"id":"https://openalex.org/I133837150","display_name":"University of Huddersfield","ror":"https://ror.org/05t1h8f27","country_code":"GB","type":"education","lineage":["https://openalex.org/I133837150"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Len Gelman","raw_affiliation_strings":["School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK","institution_ids":["https://openalex.org/I133837150"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5090855504"],"corresponding_institution_ids":["https://openalex.org/I93860229"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":3.5237,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.92732608,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"22","issue":"2","first_page":"671","last_page":"671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.998199999332428,"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":0.998199999332428,"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":0.9904000163078308,"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/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9757000207901001,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7214488983154297},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6543822288513184},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6146817803382874},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5717383623123169},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.5608047246932983},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5009360313415527},{"id":"https://openalex.org/keywords/fast-fourier-transform","display_name":"Fast Fourier transform","score":0.49416783452033997},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.46238794922828674},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4522956609725952},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42867010831832886},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.42631950974464417},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3541538119316101},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.3418828547000885},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22559866309165955},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18182477355003357},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08129265904426575},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.0732525885105133}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7214488983154297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6543822288513184},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6146817803382874},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5717383623123169},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.5608047246932983},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5009360313415527},{"id":"https://openalex.org/C75172450","wikidata":"https://www.wikidata.org/wiki/Q623950","display_name":"Fast Fourier transform","level":2,"score":0.49416783452033997},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.46238794922828674},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4522956609725952},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42867010831832886},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.42631950974464417},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3541538119316101},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.3418828547000885},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22559866309165955},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18182477355003357},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08129265904426575},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.0732525885105133},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012815","descriptor_name":"Signal Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012815","descriptor_name":"Signal Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012815","descriptor_name":"Signal Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014732","descriptor_name":"Vibration","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014732","descriptor_name":"Vibration","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014732","descriptor_name":"Vibration","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":8,"locations":[{"id":"doi:10.3390/s22020671","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22020671","pdf_url":"https://www.mdpi.com/1424-8220/22/2/671/pdf?version=1642475664","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:35062632","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35062632","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pure.atira.dk:openaire/8dff25d1-feb7-4fd5-aceb-078c298947db","is_oa":true,"landing_page_url":"https://pure.hud.ac.uk/en/publications/8dff25d1-feb7-4fd5-aceb-078c298947db","pdf_url":null,"source":{"id":"https://openalex.org/S4306402508","display_name":"Huddersfield Research Portal (University of Huddersfield)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I133837150","host_organization_name":"University of Huddersfield","host_organization_lineage":["https://openalex.org/I133837150"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Yang, D, Karimi, H R & Gelman, L 2022, 'A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks', Sensors, vol. 22, no. 2, 671. https://doi.org/10.3390/s22020671","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:doaj.org/article:1ba18d028d1e4874bdf3bf6ab97c9d26","is_oa":true,"landing_page_url":"https://doaj.org/article/1ba18d028d1e4874bdf3bf6ab97c9d26","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 2, p 671 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/2/671/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22020671","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 2; Pages: 671","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:8780327","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8780327","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:pure.atira.dk:publications/8dff25d1-feb7-4fd5-aceb-078c298947db","is_oa":true,"landing_page_url":"http://www.scopus.com/inward/record.url?scp=85122856671&partnerID=8YFLogxK","pdf_url":null,"source":{"id":"https://openalex.org/S4306402508","display_name":"Huddersfield Research Portal (University of Huddersfield)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I133837150","host_organization_name":"University of Huddersfield","host_organization_lineage":["https://openalex.org/I133837150"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Yang, D, Karimi, H R & Gelman, L 2022, 'A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks', Sensors, vol. 22, no. 2, 671. https://doi.org/10.3390/s22020671","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:re.public.polimi.it:11311/1232533","is_oa":false,"landing_page_url":"https://hdl.handle.net/11311/1232533","pdf_url":null,"source":{"id":"https://openalex.org/S4306400312","display_name":"Virtual Community of Pathological Anatomy (University of Castilla La Mancha)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79189158","host_organization_name":"University of Castilla-La Mancha","host_organization_lineage":["https://openalex.org/I79189158"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.3390/s22020671","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22020671","pdf_url":"https://www.mdpi.com/1424-8220/22/2/671/pdf?version=1642475664","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4206770926.pdf","grobid_xml":"https://content.openalex.org/works/W4206770926.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1974225813","https://openalex.org/W1994830145","https://openalex.org/W2008266158","https://openalex.org/W2058837742","https://openalex.org/W2081097942","https://openalex.org/W2092318512","https://openalex.org/W2291961022","https://openalex.org/W2530133016","https://openalex.org/W2740570963","https://openalex.org/W2767031373","https://openalex.org/W2801277493","https://openalex.org/W2807634467","https://openalex.org/W2808622270","https://openalex.org/W2887507616","https://openalex.org/W2887782657","https://openalex.org/W2892035503","https://openalex.org/W2904799515","https://openalex.org/W2910881901","https://openalex.org/W2917169831","https://openalex.org/W2956927451","https://openalex.org/W2990705538","https://openalex.org/W2999516673","https://openalex.org/W3009370740","https://openalex.org/W3025628315","https://openalex.org/W3039216919","https://openalex.org/W3041076719","https://openalex.org/W3046046004","https://openalex.org/W3081111248","https://openalex.org/W3111635293","https://openalex.org/W3113371083","https://openalex.org/W3120211578","https://openalex.org/W3122126208","https://openalex.org/W3149994345","https://openalex.org/W3153655623","https://openalex.org/W3160050649","https://openalex.org/W3165802365","https://openalex.org/W3176466636","https://openalex.org/W3178370922","https://openalex.org/W3183477965","https://openalex.org/W3200788333","https://openalex.org/W4214604401","https://openalex.org/W6786913103"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W4225639054","https://openalex.org/W1967434260","https://openalex.org/W2143985734","https://openalex.org/W2574021307","https://openalex.org/W4310813901","https://openalex.org/W1927135183"],"abstract_inverted_index":{"Some":[0],"artificial":[1,151],"intelligence":[2],"algorithms":[3,26],"have":[4],"gained":[5],"much":[6],"attention":[7],"in":[8,49],"the":[9,38,47,50,78,93,113,122,129,144,147,164,169,178],"rotating":[10],"machinery":[11],"fault":[12,152],"diagnosis":[13],"due":[14],"to":[15,37,83,111,162],"their":[16],"robust":[17],"nonlinear":[18],"regression":[19],"properties.":[20],"In":[21],"addition,":[22],"existing":[23],"deep":[24],"learning":[25],"are":[27,81,160,181],"usually":[28],"dependent":[29],"on":[30],"single":[31],"signal":[32,60,91],"features,":[33],"which":[34,135],"would":[35],"lead":[36],"loss":[39],"of":[40,46,58,96,115,124,146,168,177],"some":[41],"information":[42,48],"or":[43],"incomplete":[44],"use":[45],"signal.":[51],"To":[52,142],"address":[53],"this":[54],"problem,":[55],"three":[56],"kinds":[57],"popular":[59],"processing":[61],"methods,":[62],"including":[63],"Fast":[64],"Fourier":[65,69],"Transform":[66,70],"(FFT),":[67],"Short-Time":[68],"(STFT)":[71],"and":[72,127,155,174],"directly":[73],"slicing":[74],"one-dimensional":[75],"data":[76,95],"into":[77],"two-dimensional":[79],"matrix,":[80],"used":[82,110,161],"create":[84],"four":[85,97,116],"different":[86,137],"datasets":[87],"from":[88,138],"raw":[89],"vibration":[90],"as":[92,183],"input":[94],"enhancement":[98],"Convolutional":[99],"Neural":[100],"Networks":[101],"(CNN)":[102],"models.":[103],"Then,":[104],"a":[105,156],"fuzzy":[106],"fusion":[107,140],"strategy":[108],"is":[109,136],"fuse":[112],"output":[114],"CNN":[117],"models":[118],"that":[119],"could":[120],"analyze":[121],"importance":[123],"each":[125,133],"classifier":[126],"explore":[128],"interaction":[130],"index":[131],"between":[132],"classifier,":[134],"conventional":[139],"strategies.":[141],"show":[143],"performance":[145],"proposed":[148,179],"model,":[149],"an":[150],"bearing":[153,158],"dataset":[154,159],"real-world":[157],"test":[163],"feature":[165],"extraction":[166],"capability":[167],"model.":[170],"The":[171],"good":[172],"anti-noise":[173],"interpretation":[175],"characteristics":[176],"method":[180],"demonstrated":[182],"well.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
