{"id":"https://openalex.org/W3192426520","doi":"https://doi.org/10.1109/icphm51084.2021.9486528","title":"Temporal Convolutional Network Based Regression Approach for Estimation of Remaining Useful Life","display_name":"Temporal Convolutional Network Based Regression Approach for Estimation of Remaining Useful Life","publication_year":2021,"publication_date":"2021-06-07","ids":{"openalex":"https://openalex.org/W3192426520","doi":"https://doi.org/10.1109/icphm51084.2021.9486528","mag":"3192426520"},"language":"en","primary_location":{"id":"doi:10.1109/icphm51084.2021.9486528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm51084.2021.9486528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Prognostics and Health Management (ICPHM)","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/A5020211796","display_name":"Rongze Li","orcid":null},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Rongze Li","raw_affiliation_strings":["University of Nottingham"],"affiliations":[{"raw_affiliation_string":"University of Nottingham","institution_ids":["https://openalex.org/I142263535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042650030","display_name":"Zhengtian Chu","orcid":null},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zhengtian Chu","raw_affiliation_strings":["University of Nottingham"],"affiliations":[{"raw_affiliation_string":"University of Nottingham","institution_ids":["https://openalex.org/I142263535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037661672","display_name":"Wangkai Jin","orcid":"https://orcid.org/0000-0003-1607-2781"},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wangkai Jin","raw_affiliation_strings":["University of Nottingham,Ningbo,China","University of Nottingham, Ningbo, China"],"affiliations":[{"raw_affiliation_string":"University of Nottingham,Ningbo,China","institution_ids":["https://openalex.org/I13591777"]},{"raw_affiliation_string":"University of Nottingham, Ningbo, China","institution_ids":["https://openalex.org/I13591777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101877535","display_name":"Yaohua Wang","orcid":"https://orcid.org/0000-0002-9556-5535"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaohua Wang","raw_affiliation_strings":["National University of Defense Technology"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050750924","display_name":"Xiao Hu","orcid":"https://orcid.org/0000-0003-1128-4099"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Hu","raw_affiliation_strings":["National University of Defense Technology"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020211796"],"corresponding_institution_ids":["https://openalex.org/I142263535"],"apc_list":null,"apc_paid":null,"fwci":4.0914,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.94320242,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13690","display_name":"Quality and Safety in Healthcare","score":0.9745000004768372,"subfield":{"id":"https://openalex.org/subfields/3607","display_name":"Medical Laboratory Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T13690","display_name":"Quality and Safety in Healthcare","score":0.9745000004768372,"subfield":{"id":"https://openalex.org/subfields/3607","display_name":"Medical Laboratory Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9731000065803528,"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":0.972000002861023,"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/prognostics","display_name":"Prognostics","score":0.9478229284286499},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7293633222579956},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6404368877410889},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5923271775245667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5894537568092346},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5851358771324158},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5698524117469788},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5511330366134644},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5296242833137512},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5138350129127502},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.458500474691391},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4350176751613617},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3972260057926178},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18878638744354248}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.9478229284286499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7293633222579956},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6404368877410889},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5923271775245667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5894537568092346},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5851358771324158},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5698524117469788},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5511330366134644},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5296242833137512},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5138350129127502},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.458500474691391},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4350176751613617},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3972260057926178},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18878638744354248},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"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/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/icphm51084.2021.9486528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icphm51084.2021.9486528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Prognostics and Health Management (ICPHM)","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":34,"referenced_works":["https://openalex.org/W196214544","https://openalex.org/W1665214252","https://openalex.org/W1991501643","https://openalex.org/W2033800551","https://openalex.org/W2062181109","https://openalex.org/W2064675550","https://openalex.org/W2085659675","https://openalex.org/W2095705004","https://openalex.org/W2110787940","https://openalex.org/W2123079394","https://openalex.org/W2161850243","https://openalex.org/W2194775991","https://openalex.org/W2395611524","https://openalex.org/W2415594836","https://openalex.org/W2471161958","https://openalex.org/W2519091744","https://openalex.org/W2792764867","https://openalex.org/W2949382160","https://openalex.org/W2963840672","https://openalex.org/W2965778995","https://openalex.org/W3008143440","https://openalex.org/W3011860517","https://openalex.org/W3033830825","https://openalex.org/W3093279014","https://openalex.org/W3119685096","https://openalex.org/W3136562360","https://openalex.org/W4298255286","https://openalex.org/W6607974698","https://openalex.org/W6637242042","https://openalex.org/W6674330103","https://openalex.org/W6678334688","https://openalex.org/W6683885649","https://openalex.org/W6696085341","https://openalex.org/W6779430159"],"related_works":["https://openalex.org/W2683128","https://openalex.org/W6680660","https://openalex.org/W9190101","https://openalex.org/W13678974","https://openalex.org/W1678066","https://openalex.org/W8021486","https://openalex.org/W9333608","https://openalex.org/W1916988","https://openalex.org/W56690","https://openalex.org/W267997"],"abstract_inverted_index":{"Remaining":[0],"Useful":[1],"Life":[2],"(RUL)":[3],"is":[4],"an":[5],"essential":[6],"factor":[7],"in":[8,41,58,69,107,134,160],"the":[9,23,46,59,63,75,100,117,125,145,155,161],"Prognostics":[10],"and":[11,18,31,45,81,91,111],"Health":[12],"Management":[13],"(PHM)":[14],"field.":[15,61,163],"A":[16],"reliable":[17],"accurate":[19],"RUL":[20,135,151],"estimation":[21],"of":[22,39,48,157],"condition":[24],"monitoring":[25],"data":[26,109],"could":[27],"maximize":[28],"system":[29],"performance":[30,133],"reduce":[32],"maintenance":[33],"costs.":[34],"Recently,":[35],"with":[36],"a":[37],"surge":[38],"interest":[40],"deep":[42],"learning":[43],"(DL)":[44],"rise":[47],"computational":[49],"power,":[50],"many":[51],"state-of-the-art":[52],"neural":[53,148],"networks":[54,66,149],"have":[55,67,87,121],"been":[56],"introduced":[57],"PHM":[60,162],"However,":[62],"previously":[64],"proposed":[65,147],"drawbacks":[68],"handling":[70],"sequential":[71,108],"tasks.":[72],"For":[73],"example,":[74],"widely-used":[76],"Recurrent":[77],"Neural":[78,84],"Network":[79,85,103],"(RNN)":[80],"Long":[82],"Short-term":[83],"(LSTM)":[86],"long-term":[88],"dependency":[89],"problems":[90,114],"gradient":[92],"vanishing":[93],"problems.":[94],"In":[95],"this":[96],"paper,":[97],"we":[98],"adopt":[99],"Temporal":[101],"Convolutional":[102],"(TCN),":[104],"which":[105,153],"excels":[106],"processing":[110],"avoid":[112],"potential":[113,156],"shared":[115],"by":[116],"aforementioned":[118],"models.":[119],"We":[120],"leveraged":[122],"TCN":[123,142,158],"on":[124],"C-MAPSS":[126],"Dataset":[127],"from":[128],"NASA":[129],"to":[130],"examine":[131],"its":[132],"estimation.":[136],"Our":[137],"experiments":[138],"result":[139],"shows":[140],"that":[141],"outperforms":[143],"all":[144],"previous":[146],"for":[150],"estimation,":[152],"indicates":[154],"applications":[159]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}