{"id":"https://openalex.org/W3135690936","doi":"https://doi.org/10.1109/access.2021.3064962","title":"Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks","display_name":"Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3135690936","doi":"https://doi.org/10.1109/access.2021.3064962","mag":"3135690936"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3064962","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3064962","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09374403.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09374403.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071538550","display_name":"Hao Luo","orcid":"https://orcid.org/0000-0002-6405-4011"},"institutions":[{"id":"https://openalex.org/I118803816","display_name":"Liaoning University","ror":"https://ror.org/03xpwj629","country_code":"CN","type":"education","lineage":["https://openalex.org/I118803816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Luo","raw_affiliation_strings":["College of Information, Liaoning University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information, Liaoning University, Shenyang, China","institution_ids":["https://openalex.org/I118803816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079609442","display_name":"Chao He","orcid":"https://orcid.org/0000-0002-4666-377X"},"institutions":[{"id":"https://openalex.org/I118803816","display_name":"Liaoning University","ror":"https://ror.org/03xpwj629","country_code":"CN","type":"education","lineage":["https://openalex.org/I118803816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao He","raw_affiliation_strings":["College of Information, Liaoning University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information, Liaoning University, Shenyang, China","institution_ids":["https://openalex.org/I118803816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101140082","display_name":"Jianing Zhou","orcid":"https://orcid.org/0009-0005-4280-4706"},"institutions":[{"id":"https://openalex.org/I118803816","display_name":"Liaoning University","ror":"https://ror.org/03xpwj629","country_code":"CN","type":"education","lineage":["https://openalex.org/I118803816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianing Zhou","raw_affiliation_strings":["College of Information, Liaoning University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information, Liaoning University, Shenyang, China","institution_ids":["https://openalex.org/I118803816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109853239","display_name":"Li Zhang","orcid":"https://orcid.org/0009-0003-8194-2867"},"institutions":[{"id":"https://openalex.org/I118803816","display_name":"Liaoning University","ror":"https://ror.org/03xpwj629","country_code":"CN","type":"education","lineage":["https://openalex.org/I118803816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["College of Information, Liaoning University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information, Liaoning University, Shenyang, China","institution_ids":["https://openalex.org/I118803816"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I118803816"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.7068,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.89939377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"9","issue":null,"first_page":"42013","last_page":"42026"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9961000084877014,"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.9958000183105469,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.8269170522689819},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.6790165901184082},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6531668901443481},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6440265774726868},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5291341543197632},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49767163395881653},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39530426263809204},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38433611392974854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34231555461883545}],"concepts":[{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.8269170522689819},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.6790165901184082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6531668901443481},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6440265774726868},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5291341543197632},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49767163395881653},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39530426263809204},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38433611392974854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34231555461883545},{"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3064962","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3064962","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09374403.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c330c2efa9734308b55b4a63e3f92856","is_oa":true,"landing_page_url":"https://doaj.org/article/c330c2efa9734308b55b4a63e3f92856","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 42013-42026 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3064962","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3064962","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9312710/09374403.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4699999988079071,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3135690936.pdf","grobid_xml":"https://content.openalex.org/works/W3135690936.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1535723536","https://openalex.org/W1862394037","https://openalex.org/W1979408645","https://openalex.org/W2000982976","https://openalex.org/W2014683958","https://openalex.org/W2025697666","https://openalex.org/W2056700360","https://openalex.org/W2187089797","https://openalex.org/W2258884143","https://openalex.org/W2333775360","https://openalex.org/W2488128984","https://openalex.org/W2610099500","https://openalex.org/W2765226309","https://openalex.org/W2938286837","https://openalex.org/W2954087556","https://openalex.org/W2964121744","https://openalex.org/W2969168777","https://openalex.org/W2969849903","https://openalex.org/W2990922109","https://openalex.org/W2995167577","https://openalex.org/W3004565805","https://openalex.org/W3015801892","https://openalex.org/W3017024051","https://openalex.org/W3035386920","https://openalex.org/W3087087274","https://openalex.org/W3092375958","https://openalex.org/W3093592106","https://openalex.org/W3112747943","https://openalex.org/W3118653585","https://openalex.org/W6631190155","https://openalex.org/W6722189814"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2968645206","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W2743832667","https://openalex.org/W2969890106"],"abstract_inverted_index":{"Rolling":[0],"bearings,":[1],"as":[2],"the":[3,7,32,36,40,43,82,95,125,132,147,151,165,173,181,196,201,208,216,223,226],"main":[4],"components":[5],"of":[6,35,77,81,160,198,210],"large":[8],"industrial":[9,37,239],"rotating":[10],"equipment,":[11],"usually":[12],"work":[13],"under":[14],"complex":[15],"conditions":[16],"and":[17,61,93,106,139,150,180,215,233],"are":[18],"prone":[19],"to":[20,88,130,143,146,167,194,206],"break":[21],"down.":[22],"It":[23,220],"can":[24,187],"provide":[25],"a":[26,48,237],"certain":[27],"theoretical":[28],"basis":[29],"for":[30,97],"identifying":[31],"sub-health":[33,49,228],"state":[34],"equipment":[38],"by":[39,69,124],"analysis":[41],"from":[42,178,184],"incipient":[44,133],"weak":[45],"signals.":[46],"Thus,":[47],"recognition":[50,229],"offline":[51],"algorithm":[52,224],"based":[53],"on":[54,200],"Refined":[55],"Composite":[56],"Multiscale":[57],"Dispersion":[58],"Entropy":[59],"(RCMDE)":[60],"Deep":[62],"Belief":[63],"Network-Extreme":[64],"Learning":[65],"Machine":[66],"(DBN-ELM)":[67],"optimized":[68,123],"Improved":[70],"Firework":[71,101],"Algorithm":[72,102],"(IFWA)":[73],"is":[74,86,113,128,141,162,204,218],"proposed.":[75],"First":[76],"all,":[78],"in":[79,100,155,192,213],"light":[80],"drawbacks":[83],"that":[84,222],"it":[85],"easy":[87],"fall":[89],"into":[90,115],"local":[91],"optima":[92],"cross":[94],"boundary":[96],"exploding":[98],"fireworks":[99],"(FWA),":[103],"Cauchy":[104],"mutation":[105],"adaptive":[107],"dynamic":[108],"explosion":[109],"radius":[110],"factor":[111],"coefficient":[112],"introduced":[114],"IFWA.":[116],"Secondly,":[117],"Maximum":[118],"Correlation":[119],"Kurtosis":[120],"Deconvolution":[121],"(MCKD)":[122],"improved":[126],"parameters":[127],"used":[129,142,205],"process":[131],"vibration":[134],"signals":[135,161],"with":[136],"nonlinearity,":[137],"nonstationary,":[138],"IFWA":[140,203],"adaptively":[144],"adjust":[145],"period":[148],"T":[149],"filter":[152],"length":[153],"L":[154],"MCKD(IFWA-MCKD).":[156],"Then,":[157],"each":[158],"sequence":[159],"further":[163],"extracted":[164],"feature-RCMDE":[166],"rich":[168],"sample":[169],"diversity.":[170],"Finally,":[171],"combining":[172],"powerful":[174],"unsupervised":[175],"learning":[176],"capability":[177,183],"DBN":[179],"generalization":[182],"ELM,":[185],"DBN-ELM":[186],"be":[188],"established.":[189,219],"What's":[190],"more,":[191],"order":[193],"avoid":[195],"interference":[197],"human":[199],"parameters,":[202],"optimize":[207],"number":[209],"hidden":[211],"nodes":[212],"DBN-ELM,":[214],"IFWA-DBN-ELM":[217],"shows":[221],"has":[225,236],"higher":[227],"accuracy,":[230],"better":[231,238],"robustness":[232],"generalization,":[234],"which":[235],"application":[240],"prospect.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}