{"id":"https://openalex.org/W2696119703","doi":"https://doi.org/10.1145/3094243.3094244","title":"A self-adaptive deep belief network with Nesterov momentum for the fault diagnosis of rolling element bearings","display_name":"A self-adaptive deep belief network with Nesterov momentum for the fault diagnosis of rolling element bearings","publication_year":2017,"publication_date":"2017-06-02","ids":{"openalex":"https://openalex.org/W2696119703","doi":"https://doi.org/10.1145/3094243.3094244","mag":"2696119703"},"language":"en","primary_location":{"id":"doi:10.1145/3094243.3094244","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3094243.3094244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 International Conference on Deep Learning Technologies","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/A5044846912","display_name":"Shenghao Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenghao Tang","raw_affiliation_strings":["School of Urban Rail Transportation, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Urban Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101576727","display_name":"Wei You","orcid":"https://orcid.org/0000-0002-2601-8247"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei You","raw_affiliation_strings":["School of Urban Rail Transportation, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Urban Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091072755","display_name":"Changqing Shen","orcid":"https://orcid.org/0000-0002-5143-8366"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changqing Shen","raw_affiliation_strings":["School of Urban Rail Transportation, Soochow University, Suzhou, China, Easyway (Suzhou) Electronics Tech Co., Ltd, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Urban Rail Transportation, Soochow University, Suzhou, China, Easyway (Suzhou) Electronics Tech Co., Ltd, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043608454","display_name":"Juanjuan Shi","orcid":"https://orcid.org/0000-0001-8634-9083"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juanjuan Shi","raw_affiliation_strings":["School of Urban Rail Transportation, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Urban Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100415892","display_name":"Shuang Li","orcid":"https://orcid.org/0000-0001-9142-5036"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuang Li","raw_affiliation_strings":["School of Urban Rail Transportation, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Urban Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100758788","display_name":"Zhongkui Zhu","orcid":"https://orcid.org/0000-0001-9827-4154"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongkui Zhu","raw_affiliation_strings":["School of Urban Rail Transportation, Soochow University, Suzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Urban Rail Transportation, Soochow University, Suzhou, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4082,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.61878635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"12","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9894000291824341,"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":0.9876999855041504,"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/deep-belief-network","display_name":"Deep belief network","score":0.7859857082366943},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.703752875328064},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6988834142684937},{"id":"https://openalex.org/keywords/rolling-element-bearing","display_name":"Rolling-element bearing","score":0.6921571493148804},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6030322313308716},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.5915914177894592},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5709488987922668},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5045179128646851},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46361809968948364},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4545823335647583},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.454456090927124},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3549503982067108},{"id":"https://openalex.org/keywords/vibration","display_name":"Vibration","score":0.08226284384727478}],"concepts":[{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.7859857082366943},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.703752875328064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6988834142684937},{"id":"https://openalex.org/C2780155820","wikidata":"https://www.wikidata.org/wiki/Q1335987","display_name":"Rolling-element bearing","level":3,"score":0.6921571493148804},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6030322313308716},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.5915914177894592},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5709488987922668},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5045179128646851},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46361809968948364},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4545823335647583},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.454456090927124},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3549503982067108},{"id":"https://openalex.org/C198394728","wikidata":"https://www.wikidata.org/wiki/Q3695508","display_name":"Vibration","level":2,"score":0.08226284384727478},{"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/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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3094243.3094244","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3094243.3094244","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 International Conference on Deep Learning Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1967879920","https://openalex.org/W1980287119","https://openalex.org/W2063922127","https://openalex.org/W2089181630","https://openalex.org/W2127591698","https://openalex.org/W2146502635","https://openalex.org/W2149870286","https://openalex.org/W2153481825","https://openalex.org/W2184192902","https://openalex.org/W2200601869","https://openalex.org/W2219903032","https://openalex.org/W2485614840","https://openalex.org/W2964121744","https://openalex.org/W6600284362"],"related_works":["https://openalex.org/W2585432886","https://openalex.org/W2165991108","https://openalex.org/W3082895349","https://openalex.org/W2565516711","https://openalex.org/W2340423614","https://openalex.org/W4281808365","https://openalex.org/W3004069267","https://openalex.org/W3164007574","https://openalex.org/W4327774331","https://openalex.org/W3215044793"],"abstract_inverted_index":{"Effective":[0],"fault":[1,38,82,101,142],"diagnosis":[2,102],"of":[3,15,48,68,103,114,127],"rotating":[4],"machinery":[5],"helps":[6],"prevent":[7],"unexpected":[8],"machine":[9,65],"breakdowns":[10],"resulting":[11],"from":[12,119],"the":[13,100,125,128,135,153],"failure":[14],"essential":[16],"components.":[17],"Traditional":[18],"artificial":[19,24],"intelligence":[20],"methods,":[21],"such":[22,72],"as":[23,60,73],"neural":[25],"networks":[26],"and":[27],"support":[28],"vector":[29],"machine,":[30],"have":[31],"been":[32],"proved":[33],"to":[34,76,123,150],"be":[35],"effective":[36,78],"in":[37,50,81,96,141],"identification.":[39],"However,":[40],"extracting":[41],"features":[42,79],"manually":[43],"requires":[44],"a":[45,61,85,112,120],"high":[46],"degree":[47],"expertise":[49],"signal":[51],"processing.":[52],"Deep":[53],"belief":[54],"network":[55],"(DBN)":[56],"has":[57],"gained":[58],"popularity":[59],"new":[62],"method":[63,137,155],"for":[64,99],"learning":[66,88],"because":[67],"its":[69,74],"potential":[70],"merits":[71],"capability":[75],"extract":[77],"automatically":[80],"diagnosis.":[83],"Therefore,":[84],"novel":[86],"adaptive":[87],"rate":[89],"DBN":[90,130],"with":[91],"Nesterov":[92],"momentum":[93],"is":[94,109],"proposed":[95,129,136],"this":[97],"study":[98],"rolling":[104],"element":[105],"bearings.":[106],"An":[107],"experiment":[108],"conducted":[110,149],"using":[111],"dataset":[113],"bearing":[115],"health":[116],"states":[117],"obtained":[118],"test":[121],"rig":[122],"substantiate":[124],"utility":[126],"architecture.":[131],"Results":[132],"show":[133],"that":[134,152],"demonstrates":[138],"impressive":[139],"performance":[140],"pattern":[143],"recognition.":[144],"Comparison":[145],"analyses":[146],"are":[147],"further":[148],"demonstrate":[151],"advanced":[154],"performs":[156],"better":[157],"than":[158],"current":[159],"methods.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
