{"id":"https://openalex.org/W4362500790","doi":"https://doi.org/10.1109/tim.2023.3264027","title":"Bearing Remaining Useful Life Prediction Using Federated Learning With Taylor-Expansion Network Pruning","display_name":"Bearing Remaining Useful Life Prediction Using Federated Learning With Taylor-Expansion Network Pruning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4362500790","doi":"https://doi.org/10.1109/tim.2023.3264027"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3264027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3264027","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5101686033","display_name":"Xi Chen","orcid":"https://orcid.org/0000-0003-4468-8735"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xi Chen","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing, China","School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0003-4468-8735","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100779741","display_name":"Hui Wang","orcid":"https://orcid.org/0000-0002-4910-7824"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Wang","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing, China","School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0002-4910-7824","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037531844","display_name":"Siliang Lu","orcid":"https://orcid.org/0000-0002-3066-5623"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siliang Lu","raw_affiliation_strings":["School of Electrical Engineering and Automation, Anhui University, Hefei, China","School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China"],"raw_orcid":"https://orcid.org/0000-0002-3066-5623","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Automation, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]},{"raw_affiliation_string":"School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ruqiang Yan","orcid":"https://orcid.org/0000-0003-4341-6535"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]},{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruqiang Yan","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing, China","School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China","School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0003-4341-6535","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101686033"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":5.7389,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.96678731,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.991599977016449,"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":0.991599977016449,"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/pruning","display_name":"Pruning","score":0.8501511812210083},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7550514936447144},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.603244960308075},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5420511364936829},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5292607545852661},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5122696757316589},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4957314431667328},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49246296286582947},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4567093253135681},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.44720661640167236},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4150718152523041}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8501511812210083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7550514936447144},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.603244960308075},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5420511364936829},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5292607545852661},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5122696757316589},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4957314431667328},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49246296286582947},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4567093253135681},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.44720661640167236},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4150718152523041},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3264027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3264027","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Responsible consumption and production","id":"https://metadata.un.org/sdg/12","score":0.5099999904632568}],"awards":[{"id":"https://openalex.org/G164778973","display_name":null,"funder_award_id":"2018YFB1702400","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6471001617","display_name":null,"funder_award_id":"51835009","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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1978965153","https://openalex.org/W2535838896","https://openalex.org/W2591055632","https://openalex.org/W2592062672","https://openalex.org/W2612904117","https://openalex.org/W2772084711","https://openalex.org/W2783522756","https://openalex.org/W2805330622","https://openalex.org/W2808622270","https://openalex.org/W2900120080","https://openalex.org/W2900529838","https://openalex.org/W2904460913","https://openalex.org/W2908875359","https://openalex.org/W2963674932","https://openalex.org/W2976132861","https://openalex.org/W2977090839","https://openalex.org/W2977886818","https://openalex.org/W2982064756","https://openalex.org/W3010852232","https://openalex.org/W3014570380","https://openalex.org/W3033580259","https://openalex.org/W3101220048","https://openalex.org/W3102904972","https://openalex.org/W3115710758","https://openalex.org/W3123983671","https://openalex.org/W3129318029","https://openalex.org/W3133504636","https://openalex.org/W3135855722","https://openalex.org/W3138513656","https://openalex.org/W3150223772","https://openalex.org/W3177628906","https://openalex.org/W4297687186","https://openalex.org/W4312975111","https://openalex.org/W4318619660","https://openalex.org/W4320165363","https://openalex.org/W6638632666","https://openalex.org/W6728757088","https://openalex.org/W6739917289","https://openalex.org/W6755988804","https://openalex.org/W6768099474","https://openalex.org/W6770035337","https://openalex.org/W6849977383"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W4313339048","https://openalex.org/W4386004629","https://openalex.org/W3176734149","https://openalex.org/W3201779876","https://openalex.org/W3113627641","https://openalex.org/W2918879532"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,67,139],"of":[2,40],"bearing":[3,65],"remaining":[4],"useful":[5],"life":[6],"(RUL)":[7],"is":[8,80,117,145,163],"essential":[9],"for":[10],"machine":[11],"health":[12],"management.":[13],"In":[14,85],"existing":[15],"data-driven":[16],"prognostic":[17,230],"methods,":[18],"centralized":[19],"data":[20,32,153,180,233],"resources":[21],"and":[22,74,91,138,204],"deep":[23,133],"neural":[24,105],"networks":[25],"(DNN)":[26],"are":[27,47],"two":[28],"requisites.":[29],"However,":[30],"conventional":[31],"aggregation":[33],"may":[34],"result":[35],"in":[36,82,112,155,208,232],"the":[37,71,113,120,143,152,159,166,183,198,209,222],"privacy":[38],"disclosure":[39],"equipment.":[41],"Meanwhile,":[42],"most":[43],"DNN-based":[44],"RUL":[45,66,99,121],"predictors":[46],"over-parameterized,":[48],"making":[49],"them":[50],"hard":[51],"to":[52,96,147,165,168,181,192,213,229],"be":[53],"deployed":[54],"on":[55,70],"edge":[56],"devices.":[57],"To":[58],"deal":[59],"with":[60,107],"these":[61],"shortcomings,":[62],"a":[63,88,98,102,108,193,215,226],"new":[64,194],"method":[68,224],"based":[69],"federated":[72],"learning":[73],"Taylor-expansion":[75,160],"network":[76,106,171],"pruning,":[77],"namely":[78],"RUL-FLTNP,":[79],"proposed":[81,223],"this":[83,86],"paper.":[84],"method,":[87],"central":[89],"server":[90,187],"multiple":[92],"clients":[93],"work":[94],"together":[95],"train":[97],"predictor.":[100,122],"First,":[101],"multiscale":[103,128],"convolutional":[104],"longish":[109],"full":[110],"connection":[111],"first":[114],"layer":[115],"(LFMCNN)":[116],"designed":[118],"as":[119],"Specifically,":[123],"LFMCNN":[124],"contains":[125],"three":[126],"units,":[127],"feature":[129,134],"augmentation":[130],"module":[131,136,140],"(MFAM),":[132],"extraction":[135],"(DFEM),":[137],"(PM),":[141],"where":[142],"MFAM":[144],"used":[146],"extend":[148],"shallow":[149],"features":[150],"from":[151],"stored":[154],"each":[156,175],"client.":[157],"Next,":[158],"pruning":[161,203],"criterion":[162],"applied":[164],"DFEM":[167],"delete":[169],"unimportant":[170],"nodes,":[172],"after":[173],"which":[174],"client":[176],"utilizes":[177],"its":[178],"local":[179],"recover":[182],"pruned":[184],"model.":[185],"The":[186],"aggregates":[188],"all":[189],"rebirth":[190,205],"models":[191],"global":[195],"model":[196,210],"using":[197],"Federated":[199],"Averaging":[200],"algorithm.":[201],"Network":[202],"occur":[206],"alternately":[207],"training":[211],"process":[212],"produce":[214],"compact":[216],"structure.":[217],"Experimental":[218],"results":[219],"indicate":[220],"that":[221],"provides":[225],"promising":[227],"solution":[228],"problems":[231],"privacy-preserving":[234],"scenarios.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
