{"id":"https://openalex.org/W7164343348","doi":"https://doi.org/10.3390/info17060580","title":"Fault Classification Method for Rotating Machinery Based on Hybrid Model of CWT and CNN-DOA-LSSVM","display_name":"Fault Classification Method for Rotating Machinery Based on Hybrid Model of CWT and CNN-DOA-LSSVM","publication_year":2026,"publication_date":"2026-06-11","ids":{"openalex":"https://openalex.org/W7164343348","doi":"https://doi.org/10.3390/info17060580"},"language":"en","primary_location":{"id":"doi:10.3390/info17060580","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17060580","pdf_url":null,"source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"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":"Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/info17060580","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138429097","display_name":"Liping Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liping Wang","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450000, China","institution_ids":["https://openalex.org/I23171815"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101275983","display_name":"Yingtong Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingtong Yao","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450000, China","institution_ids":["https://openalex.org/I23171815"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079767254","display_name":"Dongyao Zou","orcid":"https://orcid.org/0000-0003-1025-5974"},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongyao Zou","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450000, China"],"raw_orcid":"https://orcid.org/0000-0003-1025-5974","affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450000, China","institution_ids":["https://openalex.org/I23171815"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100744746","display_name":"Nana Li","orcid":"https://orcid.org/0000-0002-9740-0558"},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nana Li","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450000, China","institution_ids":["https://openalex.org/I23171815"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I23171815"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1782},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1782},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68878138,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"6","first_page":"580","last_page":"580"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9941999912261963,"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":0.9941999912261963,"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":0.000699999975040555,"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/T10188","display_name":"Advanced machining processes and optimization","score":0.00039999998989515007,"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/softmax-function","display_name":"Softmax function","score":0.8622999787330627},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5946000218391418},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5796999931335449},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.527899980545044},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4496000111103058},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44589999318122864},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.43970000743865967},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4065000116825104}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8622999787330627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6890000104904175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5946000218391418},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5796999931335449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5559999942779541},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.527899980545044},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4496000111103058},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4065000116825104},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3652999997138977},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3546000123023987},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.3515999913215637},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.34380000829696655},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.3337000012397766},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3165999948978424},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29660001397132874},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.2822999954223633},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/info17060580","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17060580","pdf_url":null,"source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"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":"Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:40e187393a9c438180f304f10afa7706","is_oa":false,"landing_page_url":"https://doaj.org/article/40e187393a9c438180f304f10afa7706","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information, Vol 17, Iss 6, p 580 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/info17060580","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info17060580","pdf_url":null,"source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"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":"Information","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5451806783676147,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1596717185","https://openalex.org/W2112796928","https://openalex.org/W2132984323","https://openalex.org/W2744790985","https://openalex.org/W2768753204","https://openalex.org/W2887782657","https://openalex.org/W4224935930","https://openalex.org/W4389328646","https://openalex.org/W4391247724","https://openalex.org/W4391280612","https://openalex.org/W4401597594","https://openalex.org/W4402595242","https://openalex.org/W4406212478","https://openalex.org/W4409156284","https://openalex.org/W4410944659","https://openalex.org/W4411142926","https://openalex.org/W4412602534","https://openalex.org/W4413306441","https://openalex.org/W4413816906","https://openalex.org/W4413876779","https://openalex.org/W4414208855","https://openalex.org/W4416575757","https://openalex.org/W7133839909","https://openalex.org/W7134247906","https://openalex.org/W7152747236"],"related_works":[],"abstract_inverted_index":{"Traditional":[0],"signal":[1],"processing":[2],"methods":[3],"for":[4],"rotating":[5],"machinery":[6],"fault":[7,40],"diagnosis":[8,41],"rely":[9],"heavily":[10],"on":[11,44,140],"human":[12],"experience,":[13],"while":[14],"deep":[15,73],"learning":[16],"models":[17],"often":[18],"suffer":[19],"from":[20],"unstable":[21],"classification":[22,89,163,175],"boundaries":[23],"and":[24,46,114,123,146,152,167,177],"poor":[25],"generalization":[26],"under":[27],"complex":[28],"operating":[29],"conditions.":[30],"To":[31],"address":[32],"these":[33],"issues,":[34],"this":[35],"paper":[36],"proposes":[37],"a":[38],"hybrid":[39],"method":[42,160],"based":[43],"CWT":[45,49],"CNN-DOA-LSSVM.":[47],"Firstly,":[48],"is":[50,136],"employed":[51],"to":[52,94],"convert":[53],"one-dimensional":[54],"vibration":[55],"signals":[56],"into":[57],"high-resolution":[58],"time-frequency":[59],"maps,":[60],"fully":[61],"highlighting":[62],"the":[63,77,87,96,99,108,131,141,147,158],"transient":[64],"impact":[65],"features":[66],"of":[67,80,98,112,130,134,150,165],"faults.":[68],"Secondly,":[69],"CNNs":[70,93],"automatically":[71],"extract":[72],"discriminative":[74],"features,":[75],"avoiding":[76],"cumbersome":[78],"process":[79],"manual":[81],"feature":[82],"engineering.":[83],"Thirdly,":[84],"LSSVM":[85,135],"replaces":[86],"Softmax":[88,100],"layer":[90],"in":[91,102,116,173],"traditional":[92],"overcome":[95],"deficiency":[97],"classifier":[101],"nonlinear":[103],"classification.":[104],"Finally,":[105],"by":[106],"leveraging":[107],"two-stage":[109],"separation":[110],"mechanism":[111],"exploration":[113],"exploitation":[115],"DOA,":[117],"along":[118],"with":[119],"its":[120],"unique":[121],"forgetting-supplement":[122],"dream-sharing":[124],"strategies,":[125],"an":[126],"adaptive":[127],"optimal":[128],"configuration":[129],"key":[132],"parameters":[133],"achieved.":[137],"Validation":[138],"results":[139],"Southeast":[142],"University":[143,149],"gearbox":[144],"dataset":[145,155],"Huazhong":[148],"Science":[151],"Technology":[153],"bearing":[154],"show":[156],"that":[157],"proposed":[159],"achieves":[161],"average":[162],"accuracies":[164],"99.59%":[166],"99.50%,":[168],"respectively,":[169],"demonstrating":[170],"good":[171],"performance":[172],"both":[174],"accuracy":[176],"stability.":[178]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-06-12T00:00:00"}