{"id":"https://openalex.org/W4388820502","doi":"https://doi.org/10.1109/icnsc58704.2023.10318980","title":"LSTM-based digital twin prediction model for engine life","display_name":"LSTM-based digital twin prediction model for engine life","publication_year":2023,"publication_date":"2023-10-25","ids":{"openalex":"https://openalex.org/W4388820502","doi":"https://doi.org/10.1109/icnsc58704.2023.10318980"},"language":"en","primary_location":{"id":"doi:10.1109/icnsc58704.2023.10318980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc58704.2023.10318980","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Networking, Sensing and Control (ICNSC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100450058","display_name":"Junfeng Li","orcid":"https://orcid.org/0000-0002-6272-9169"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfeng Li","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Automation,Nanjing,China","School of Automation, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Automation,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]},{"raw_affiliation_string":"School of Automation, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040333383","display_name":"Jianyu Wang","orcid":"https://orcid.org/0000-0001-9020-563X"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianyu Wang","raw_affiliation_strings":["Nanjing University of Science and Technology,School of Automation,Nanjing,China","School of Automation, Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology,School of Automation,Nanjing,China","institution_ids":["https://openalex.org/I36399199"]},{"raw_affiliation_string":"School of Automation, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9595999717712402,"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"}},"topics":[{"id":"https://openalex.org/T13891","display_name":"Engineering Diagnostics and Reliability","score":0.9595999717712402,"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"}},{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.944599986076355,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9329000115394592,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/turbofan","display_name":"Turbofan","score":0.8230956196784973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6624711155891418},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6354102492332458},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5426169037818909},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5049481987953186},{"id":"https://openalex.org/keywords/search-engine","display_name":"Search engine","score":0.4789641201496124},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4181666374206543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37711381912231445},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2606481611728668},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.17598748207092285},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.10115626454353333}],"concepts":[{"id":"https://openalex.org/C110050840","wikidata":"https://www.wikidata.org/wiki/Q654051","display_name":"Turbofan","level":2,"score":0.8230956196784973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6624711155891418},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6354102492332458},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5426169037818909},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5049481987953186},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.4789641201496124},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4181666374206543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37711381912231445},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2606481611728668},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.17598748207092285},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.10115626454353333},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"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/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"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":1,"locations":[{"id":"doi:10.1109/icnsc58704.2023.10318980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnsc58704.2023.10318980","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Networking, Sensing and Control (ICNSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/12","score":0.5799999833106995,"display_name":"Responsible consumption and production"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W809430598","https://openalex.org/W1522301498","https://openalex.org/W2064064598","https://openalex.org/W2080198418","https://openalex.org/W2120841219","https://openalex.org/W2160291974","https://openalex.org/W2518003110","https://openalex.org/W3083590190","https://openalex.org/W3204027154","https://openalex.org/W6623041627","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W4233568029","https://openalex.org/W2967774773","https://openalex.org/W3166075233","https://openalex.org/W2373556961","https://openalex.org/W2375127734","https://openalex.org/W4386141271","https://openalex.org/W2074007942","https://openalex.org/W837305724","https://openalex.org/W2380048437","https://openalex.org/W2372829958"],"abstract_inverted_index":{"As":[0],"the":[1,6,16,43,71,84,91,103,109,118,127,134,137,145,154,167,170],"key":[2],"link":[3],"to":[4,19,61,69,83,101,125,132,165],"realize":[5],"function":[7],"drive":[8],"of":[9,112,122,136,157,169],"mechanical":[10],"equipment":[11],"and":[12,23,26,34,36,51,75,88,108,150,152,159],"its":[13],"control":[14,65],"system,":[15],"engine":[17,47,64,72,85,104,139],"needs":[18],"have":[20],"good":[21],"operation":[22],"fault":[24],"diagnosis":[25],"maintenance":[27],"capability.":[28],"Based":[29],"on":[30],"digital":[31,58],"twin":[32,59],"technology":[33,60],"long":[35],"short":[37],"term":[38],"memory":[39],"recurrent":[40],"neural":[41,99],"network,":[42],"article":[44],"proposes":[45],"an":[46,63],"fatigue":[48,76],"monitoring":[49],"method":[50,56],"life":[52,78,105,140],"prediction":[53,106,135,155],"model.":[54],"The":[55],"uses":[57],"establish":[62],"system":[66],"simulation":[67,163],"model":[68,114],"obtain":[70],"state":[73],"parameters":[74],"value":[77],"related":[79],"data":[80,93,111,120],"set":[81,94,121],"according":[82],"composition":[86],"structure":[87],"working":[89],"principle;":[90],"obtained":[92],"is":[95,115],"trained":[96],"by":[97],"LSTM":[98,123],"network":[100,124],"determine":[102],"model,":[107],"real-time":[110],"DT":[113],"used":[116],"as":[117,131],"test":[119],"complete":[126],"testing":[128],"work,":[129],"so":[130],"achieve":[133],"remaining":[138],"results.":[141],"Finally,":[142],"we":[143],"select":[144],"Turbofan":[146],"Engine":[147],"Degradation":[148],"dataset":[149],"compare":[151],"analyze":[153],"results":[156,164],"BP":[158],"RNN":[160],"networks":[161],"through":[162],"prove":[166],"effectiveness":[168],"proposed":[171],"method.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
