{"id":"https://openalex.org/W4283732943","doi":"https://doi.org/10.1109/codit55151.2022.9804032","title":"Remaining Useful Life Predictions for Turbofan Engine Using Semi-supervised DBN-LSTM Joint Training Model","display_name":"Remaining Useful Life Predictions for Turbofan Engine Using Semi-supervised DBN-LSTM Joint Training Model","publication_year":2022,"publication_date":"2022-05-17","ids":{"openalex":"https://openalex.org/W4283732943","doi":"https://doi.org/10.1109/codit55151.2022.9804032"},"language":"en","primary_location":{"id":"doi:10.1109/codit55151.2022.9804032","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit55151.2022.9804032","pdf_url":null,"source":{"id":"https://openalex.org/S4363607900","display_name":"2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)","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/A5100766524","display_name":"Yutong Zhang","orcid":"https://orcid.org/0000-0001-8275-9608"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yutong Zhang","raw_affiliation_strings":["College of Control Science and Engineering, Zhe-jiang University,State Key Laboratory of Industrial Control Technology,Hangzhou,China,310027"],"affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, Zhe-jiang University,State Key Laboratory of Industrial Control Technology,Hangzhou,China,310027","institution_ids":["https://openalex.org/I76130692","https://openalex.org/I4391767838"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaochu Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaochu Tang","raw_affiliation_strings":["College of Control Science and Engineering, Zhe-jiang University,State Key Laboratory of Industrial Control Technology,Hangzhou,China,310027"],"affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, Zhe-jiang University,State Key Laboratory of Industrial Control Technology,Hangzhou,China,310027","institution_ids":["https://openalex.org/I76130692","https://openalex.org/I4391767838"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100654679","display_name":"Xinmin Zhang","orcid":"https://orcid.org/0000-0002-4761-3969"},"institutions":[{"id":"https://openalex.org/I4391767838","display_name":"State Key Laboratory of Industrial Control Technology","ror":"https://ror.org/03a33a786","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391767838","https://openalex.org/I76130692"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinmin Zhang","raw_affiliation_strings":["College of Control Science and Engineering, Zhe-jiang University,State Key Laboratory of Industrial Control Technology,Hangzhou,China,310027"],"affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, Zhe-jiang University,State Key Laboratory of Industrial Control Technology,Hangzhou,China,310027","institution_ids":["https://openalex.org/I76130692","https://openalex.org/I4391767838"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100766524"],"corresponding_institution_ids":["https://openalex.org/I4391767838","https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":0.7055,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58161728,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"94","last_page":"99"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9930999875068665,"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":0.9930999875068665,"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":0.9850000143051147,"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/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9761000275611877,"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/computer-science","display_name":"Computer science","score":0.6722224354743958},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6375020742416382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6265502572059631},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.6052461862564087},{"id":"https://openalex.org/keywords/prognostics","display_name":"Prognostics","score":0.6017905473709106},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5521876215934753},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5480005741119385},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4923611879348755},{"id":"https://openalex.org/keywords/turbofan","display_name":"Turbofan","score":0.4749041795730591},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.42992833256721497},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4292938709259033},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4235474467277527},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3548395037651062},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22359684109687805}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6722224354743958},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6375020742416382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6265502572059631},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.6052461862564087},{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.6017905473709106},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5521876215934753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5480005741119385},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4923611879348755},{"id":"https://openalex.org/C110050840","wikidata":"https://www.wikidata.org/wiki/Q654051","display_name":"Turbofan","level":2,"score":0.4749041795730591},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.42992833256721497},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4292938709259033},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4235474467277527},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3548395037651062},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22359684109687805},{"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/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/codit55151.2022.9804032","is_oa":false,"landing_page_url":"https://doi.org/10.1109/codit55151.2022.9804032","pdf_url":null,"source":{"id":"https://openalex.org/S4363607900","display_name":"2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)","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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/12","score":0.4300000071525574,"display_name":"Responsible consumption and production"}],"awards":[{"id":"https://openalex.org/G1988872666","display_name":null,"funder_award_id":"62003301","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2083022762","https://openalex.org/W2187873394","https://openalex.org/W2415594836","https://openalex.org/W2555297092","https://openalex.org/W2564947831","https://openalex.org/W2647889534","https://openalex.org/W2799972844","https://openalex.org/W2805330622","https://openalex.org/W2939500457","https://openalex.org/W2961350108","https://openalex.org/W3001566134","https://openalex.org/W3027554678","https://openalex.org/W3038243112","https://openalex.org/W3046805716","https://openalex.org/W3104794984","https://openalex.org/W6716485080","https://openalex.org/W6780186856"],"related_works":["https://openalex.org/W4233568029","https://openalex.org/W2967774773","https://openalex.org/W3166075233","https://openalex.org/W2373556961","https://openalex.org/W2310476526","https://openalex.org/W2375127734","https://openalex.org/W4386141271","https://openalex.org/W3213192587","https://openalex.org/W4200169289","https://openalex.org/W3199798152"],"abstract_inverted_index":{"In":[0,60],"the":[1,4,7,12,23,26,64,74,93,98,118,140,189,193,198,228,234,242],"industrial":[2,68,144],"field,":[3],"health":[5,24,30,39,58,136,190],"of":[6,14,25,35,42,67,76,114,188,192,200,213,227],"machine":[8],"may":[9],"decline":[10],"in":[11,51,111,165,177],"process":[13],"working,":[15],"so":[16],"it":[17],"is":[18,150,163,223,231,245],"necessary":[19],"to":[20,85,247],"regularly":[21],"maintain":[22],"machine.":[27],"However,":[28,92],"traditional":[29],"maintenance":[31],"methods":[32],"have":[33],"problems":[34],"insufficient":[36],"or":[37],"redundant":[38],"maintenance.":[40],"Predictions":[41],"remaining":[43],"useful":[44],"life":[45],"(RUL)":[46],"play":[47],"a":[48,112,127,157,207],"vital":[49],"role":[50],"realizing":[52],"more":[53],"accurate":[54],"system":[55],"monitoring":[56],"and":[57,71,80,105,173,218],"management.":[59],"recent":[61],"years,":[62],"with":[63],"rapid":[65],"development":[66],"big":[69],"data":[70,145,171],"deep":[72,81,129,214],"learning,":[73],"use":[75],"multi-sensor":[77],"equipment":[78],"information":[79,138],"learning":[82,130],"neural":[83,131],"network":[84,132,216],"predict":[86],"RUL":[87,95,106,159,174,201],"has":[88],"made":[89],"significant":[90],"progress.":[91],"current":[94],"prediction":[96,107,124,161,175],"faces":[97],"following":[99],"challenges:":[100],"(1)":[101],"Separate":[102],"Data":[103],"fusion":[104,172],"steps":[108],"usually":[109],"result":[110],"lack":[113],"internal":[115],"connection":[116],"between":[117],"two":[119],"models;":[120],"(2)":[121],"The":[122,143,168,225,237],"end-to-end":[123],"method":[125,230,244],"using":[126],"single":[128],"does":[133],"not":[134,183],"provide":[135],"indicator":[137],"about":[139],"degradation.":[141],"(3)":[142],"available":[146],"for":[147,179],"model":[148,162,211],"training":[149,210],"still":[151],"insufficient.":[152],"To":[153],"overcome":[154],"these":[155],"shortcomings,":[156],"new":[158],"joint":[160,209],"proposed":[164,229,243],"this":[166,205],"work.":[167],"framework":[169],"combines":[170],"models":[176],"series":[178],"simultaneous":[180],"training,":[181],"which":[182],"only":[184],"provides":[185],"continuous":[186],"visualization":[187],"degradation":[191],"system,":[194],"but":[195],"also":[196],"ensures":[197],"efficiency":[199],"prediction.":[202],"Based":[203],"on":[204],"framework,":[206],"semi-supervised":[208],"composed":[212],"belief":[215],"(DBN)":[217],"long":[219],"short-term":[220],"memory":[221],"(LSTM)":[222],"designed.":[224],"effectiveness":[226],"verified":[232],"through":[233],"C-MAPSS":[235],"dataset.":[236],"application":[238],"results":[239],"demonstrated":[240],"that":[241],"superior":[246],"other":[248],"state-of-the-art":[249],"methods.":[250]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
