{"id":"https://openalex.org/W3040017772","doi":"https://doi.org/10.1109/i2mtc43012.2020.9128699","title":"Research on Bearing Fault Diagnosis Method Based on Two-Dimensional Convolutional Neural Network","display_name":"Research on Bearing Fault Diagnosis Method Based on Two-Dimensional Convolutional Neural Network","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3040017772","doi":"https://doi.org/10.1109/i2mtc43012.2020.9128699","mag":"3040017772"},"language":"en","primary_location":{"id":"doi:10.1109/i2mtc43012.2020.9128699","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc43012.2020.9128699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","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/A5100449568","display_name":"Yuhang Wang","orcid":"https://orcid.org/0000-0002-3238-5449"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuhang Wang","raw_affiliation_strings":["School of Electrical Engineering, Beijing Jiaotong University, Beijing, China","Beijing Jiaotong University,School of Electrical Engineering,,Beijing,,China"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Jiaotong University,School of Electrical Engineering,,Beijing,,China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101973274","display_name":"Hesheng Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He-sheng ZHANG","raw_affiliation_strings":["School of Electrical Engineering, Beijing Jiaotong University, Beijing, China","Beijing Jiaotong University,School of Electrical Engineering,,Beijing,,China"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Jiaotong University,School of Electrical Engineering,,Beijing,,China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063532027","display_name":"Xiaotao Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaotao Hu","raw_affiliation_strings":["School of Electrical Engineering, Beijing Jiaotong University, Beijing, China","Beijing Jiaotong University,School of Electrical Engineering,,Beijing,,China"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"Beijing Jiaotong University,School of Electrical Engineering,,Beijing,,China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100449568"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.4461,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.61882248,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"2020","issue":null,"first_page":"1","last_page":"6"},"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/T11062","display_name":"Gear and Bearing Dynamics Analysis","score":0.9950000047683716,"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":0.964900016784668,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7828460931777954},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7816491723060608},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.6785855293273926},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6501519083976746},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.58447265625},{"id":"https://openalex.org/keywords/bearing","display_name":"Bearing (navigation)","score":0.5824486017227173},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.582342803478241},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5507850646972656},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5463330149650574},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4837177097797394},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44803452491760254},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.44190019369125366},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.43781906366348267},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3948237895965576},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3202160596847534},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06301206350326538}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7828460931777954},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7816491723060608},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.6785855293273926},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6501519083976746},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.58447265625},{"id":"https://openalex.org/C199978012","wikidata":"https://www.wikidata.org/wiki/Q1273815","display_name":"Bearing (navigation)","level":2,"score":0.5824486017227173},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.582342803478241},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5507850646972656},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5463330149650574},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4837177097797394},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44803452491760254},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.44190019369125366},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.43781906366348267},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3948237895965576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3202160596847534},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06301206350326538},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/i2mtc43012.2020.9128699","is_oa":false,"landing_page_url":"https://doi.org/10.1109/i2mtc43012.2020.9128699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","raw_type":"proceedings-article"},{"id":"mag:3209707519","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002278067456071","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2219903032","https://openalex.org/W2485614840","https://openalex.org/W2584994008","https://openalex.org/W2744686084","https://openalex.org/W2762841298","https://openalex.org/W2768753204","https://openalex.org/W2782766333","https://openalex.org/W2906110081","https://openalex.org/W2914776782","https://openalex.org/W2931331224","https://openalex.org/W2947645980"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W2123376283","https://openalex.org/W4387327236","https://openalex.org/W2183488467","https://openalex.org/W1990237101","https://openalex.org/W4309907966","https://openalex.org/W4387896287","https://openalex.org/W2187490799","https://openalex.org/W4300172249","https://openalex.org/W3170838353"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,37,40,45,48,55,58,81,84,105],"problem":[3],"that":[4,91],"traditional":[5],"bearing":[6,23,106],"fault":[7,24,42,63,97],"diagnosis":[8,25,64],"methods":[9],"rely":[10],"on":[11,28,104],"artificial":[12],"feature":[13,72],"extraction":[14],"and":[15,57,74,109],"expert":[16],"experience,":[17],"this":[18,92],"paper":[19],"proposes":[20],"an":[21],"adaptive":[22,70],"method":[26,93],"based":[27],"two-dimensional":[29,59],"convolutional":[30,60],"neural":[31,61],"network.":[32],"In":[33],"order":[34],"to":[35,44,68,79],"retain":[36],"features":[38],"of":[39,83,100],"original":[41,49],"data":[43,107],"greatest":[46],"extent,":[47],"signal":[50],"is":[51,66,118],"directly":[52],"used":[53,67,78],"as":[54],"input,":[56],"network":[62],"model":[65],"perform":[69],"hierarchical":[71],"extraction,":[73],"optimization":[75],"algorithms":[76],"are":[77],"improve":[80],"performance":[82,113],"test":[85],"set.":[86],"The":[87],"experimental":[88],"results":[89],"show":[90],"can":[94],"achieve":[95],"a":[96],"recognition":[98],"rate":[99],"more":[101],"than":[102],"99%":[103],"set,":[108],"shows":[110],"good":[111],"generalization":[112],"under":[114],"different":[115],"loads,":[116],"which":[117],"feasible":[119],"for":[120],"practical":[121],"applications.":[122]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
