{"id":"https://openalex.org/W3016195914","doi":"https://doi.org/10.1109/access.2020.2986419","title":"Deep Cost Adaptive Convolutional Network: A Classification Method for Imbalanced Mechanical Data","display_name":"Deep Cost Adaptive Convolutional Network: A Classification Method for Imbalanced Mechanical Data","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3016195914","doi":"https://doi.org/10.1109/access.2020.2986419","mag":"3016195914"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2986419","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2986419","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09058686.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/ielx7/6287639/8948470/09058686.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000283744","display_name":"Xun Dong","orcid":"https://orcid.org/0000-0001-7075-9336"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xun Dong","raw_affiliation_strings":["Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055401422","display_name":"Hongli Gao","orcid":"https://orcid.org/0000-0002-9288-4418"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongli Gao","raw_affiliation_strings":["Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-9288-4418","affiliations":[{"raw_affiliation_string":"Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066005897","display_name":"Liang Guo","orcid":"https://orcid.org/0000-0001-5338-4958"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Guo","raw_affiliation_strings":["Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045584566","display_name":"Kesi Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kesi Li","raw_affiliation_strings":["Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064945387","display_name":"Andongzhe Duan","orcid":"https://orcid.org/0000-0002-5680-6033"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Andongzhe Duan","raw_affiliation_strings":["Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-5680-6033","affiliations":[{"raw_affiliation_string":"Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]},{"raw_affiliation_string":"School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.79,"has_fulltext":true,"cited_by_count":48,"citation_normalized_percentile":{"value":0.93523558,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"71486","last_page":"71496"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9850000143051147,"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":0.9850000143051147,"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/T13050","display_name":"Oil and Gas Production Techniques","score":0.9661999940872192,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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":0.9620000123977661,"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":0.7908642888069153},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7809672355651855},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.716070294380188},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6981821656227112},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6745743155479431},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5715650320053101},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5684661269187927},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.48245078325271606},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47537627816200256},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.41459399461746216},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4037734866142273},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36522912979125977},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07428500056266785}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7908642888069153},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7809672355651855},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.716070294380188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6981821656227112},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6745743155479431},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5715650320053101},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5684661269187927},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.48245078325271606},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47537627816200256},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.41459399461746216},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4037734866142273},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36522912979125977},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07428500056266785},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2986419","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2986419","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09058686.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:c26d1ec36f4f4c61b5bcc96fb0d8b1ea","is_oa":true,"landing_page_url":"https://doaj.org/article/c26d1ec36f4f4c61b5bcc96fb0d8b1ea","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 8, Pp 71486-71496 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2986419","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2986419","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09058686.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":[{"id":"https://metadata.un.org/sdg/9","score":0.41999998688697815,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G5355358981","display_name":null,"funder_award_id":"2682017ZDPY09","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6701230860","display_name":null,"funder_award_id":"2682019CX35","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G876186815","display_name":null,"funder_award_id":"51775452","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8936803862","display_name":null,"funder_award_id":"51905452","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3016195914.pdf","grobid_xml":"https://content.openalex.org/works/W3016195914.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1563938718","https://openalex.org/W2100495367","https://openalex.org/W2118978333","https://openalex.org/W2139391008","https://openalex.org/W2158899491","https://openalex.org/W2160815625","https://openalex.org/W2164330572","https://openalex.org/W2184045248","https://openalex.org/W2185967890","https://openalex.org/W2194775991","https://openalex.org/W2273585068","https://openalex.org/W2274246713","https://openalex.org/W2508881195","https://openalex.org/W2530133016","https://openalex.org/W2591055632","https://openalex.org/W2612634114","https://openalex.org/W2735326783","https://openalex.org/W2784378279","https://openalex.org/W2792018332","https://openalex.org/W2794869810","https://openalex.org/W2795550564","https://openalex.org/W2806122632","https://openalex.org/W2898375427","https://openalex.org/W2902145350","https://openalex.org/W2952230511","https://openalex.org/W2971413776","https://openalex.org/W3004133531","https://openalex.org/W3005815918","https://openalex.org/W6683738474","https://openalex.org/W6728255800"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W4401096132","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W2964954556","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Intelligent":[0],"diagnosis":[1,9],"is":[2,56,92,123,189,215,218],"an":[3,108],"important":[4],"manner":[5],"for":[6,112,169],"mechanical":[7,49,114,136,151,184,225],"fault":[8,51,80],"in":[10,24,48,135,176],"the":[11,54,69,73,79,85,89,96,105,159,182,212,223],"era":[12],"of":[13,28,71,88],"industrial":[14],"big":[15],"data,":[16],"and":[17,32,78,142,172,194,202,217],"deep":[18,39],"network":[19],"has":[20,45],"received":[21],"extensive":[22],"attention":[23],"this":[25,127],"field":[26],"because":[27],"automatically":[29],"learning":[30,40],"features":[31],"classifying":[33],"entered":[34,58],"samples.":[35],"As":[36],"a":[37],"classic":[38],"model,":[41,128],"Convolutional":[42,100,121],"Neural":[43,101],"Network":[44,122],"been":[46],"applied":[47],"intelligent":[50,109],"diagnosis.":[52],"However,":[53],"limitation":[55],"that":[57,211],"samples":[59,75,81],"must":[60],"be":[61],"balanced":[62],"to":[63,126,150,179,220],"achieve":[64],"satisfactory":[65],"recognition":[66,86],"rate.":[67],"During":[68],"operation":[70],"machinery,":[72],"normal":[74],"are":[76,82],"abundant":[77],"rare.":[83],"Therefore,":[84],"rate":[87],"minority":[90],"category":[91],"minor":[93],"when":[94],"processing":[95],"imbalanced":[97,113,183,200,224],"data":[98,115,193,197],"with":[99,198,204],"Network.":[102],"To":[103],"solve":[104],"above":[106],"problem,":[107],"classification":[110],"method":[111,188,214],"based":[116],"on":[117],"Deep":[118],"Cost":[119],"Adaptive":[120],"proposed.":[124],"According":[125],"first,":[129],"it":[130,146],"learns":[131],"intrinsic":[132],"state":[133],"characteristics":[134,149],"raw":[137],"signals":[138],"through":[139],"multiple":[140],"convolution":[141],"pooling":[143],"operations.":[144],"Second,":[145],"maps":[147],"these":[148],"health":[152],"condition":[153],"by":[154,191],"fully":[155],"connected":[156],"layers.":[157],"Finally,":[158],"cost":[160],"adaptive":[161],"loss":[162],"function":[163],"adaptively":[164],"assigns":[165],"different":[166,199],"misclassification":[167],"costs":[168],"all":[170],"categories":[171],"keeps":[173],"updating":[174],"them":[175],"training":[177],"process":[178],"effectively":[180,221],"classify":[181,222],"data.":[185,226],"The":[186,207],"proposed":[187,213],"verified":[190],"bearing":[192],"milling":[195],"cutter":[196],"ratio,":[201],"compared":[203],"other":[205],"methods.":[206],"experimental":[208],"results":[209],"show":[210],"robust":[216],"able":[219]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}