{"id":"https://openalex.org/W4382645144","doi":"https://doi.org/10.1109/access.2023.3277730","title":"Novel Transformer-based Fusion Models for Aero-engine Remaining Useful Life Estimation","display_name":"Novel Transformer-based Fusion Models for Aero-engine Remaining Useful Life Estimation","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4382645144","doi":"https://doi.org/10.1109/access.2023.3277730"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3277730","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3277730","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10129186.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":null,"license_id":null,"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/ielx7/6287639/10005208/10129186.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010720671","display_name":"Qiankun Hu","orcid":"https://orcid.org/0000-0002-0568-4961"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qiankun Hu","raw_affiliation_strings":["College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-0568-4961","affiliations":[{"raw_affiliation_string":"College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044023674","display_name":"Yong-Ping Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongping Zhao","raw_affiliation_strings":["College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-3310-1329","affiliations":[{"raw_affiliation_string":"College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053608862","display_name":"Li-Hua Ren","orcid":"https://orcid.org/0000-0002-3348-6161"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihua Ren","raw_affiliation_strings":["College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-3348-6161","affiliations":[{"raw_affiliation_string":"College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010720671"],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.4075,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.92733964,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9926000237464905,"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":0.9926000237464905,"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":0.9718999862670898,"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/T11357","display_name":"Risk and Safety Analysis","score":0.9686999917030334,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6998633146286011},{"id":"https://openalex.org/keywords/aero-engine","display_name":"Aero engine","score":0.6726564168930054},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6517679691314697},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6378839612007141},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5784019231796265},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5099495649337769},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4906963109970093},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4437119960784912},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4431621730327606},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.417338490486145},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.41724878549575806},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3943392038345337},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.27083641290664673},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19086387753486633}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6998633146286011},{"id":"https://openalex.org/C2985438705","wikidata":"https://www.wikidata.org/wiki/Q743004","display_name":"Aero engine","level":2,"score":0.6726564168930054},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6517679691314697},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6378839612007141},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5784019231796265},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5099495649337769},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4906963109970093},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4437119960784912},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4431621730327606},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.417338490486145},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.41724878549575806},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3943392038345337},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.27083641290664673},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19086387753486633},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"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/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3277730","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3277730","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10129186.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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:52ba69cdcc91405fa6a8195f7c668891","is_oa":true,"landing_page_url":"https://doaj.org/article/52ba69cdcc91405fa6a8195f7c668891","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 52668-52685 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3277730","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3277730","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10129186.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":null,"license_id":null,"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/12","display_name":"Responsible consumption and production","score":0.5099999904632568}],"awards":[{"id":"https://openalex.org/G1176662780","display_name":null,"funder_award_id":"NS2022027","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6807314005","display_name":null,"funder_award_id":"J2019-I-0010-0010","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8129452754","display_name":null,"funder_award_id":"J2019-I-0010-0010","funder_id":"https://openalex.org/F4320329860","funder_display_name":"National Science and Technology Major Project"}],"funders":[{"id":"https://openalex.org/F4320329860","display_name":"National Science and Technology Major Project","ror":null},{"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/W4382645144.pdf","grobid_xml":"https://content.openalex.org/works/W4382645144.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2022621390","https://openalex.org/W2035109300","https://openalex.org/W2037250283","https://openalex.org/W2064675550","https://openalex.org/W2110787940","https://openalex.org/W2118020555","https://openalex.org/W2120841219","https://openalex.org/W2134456669","https://openalex.org/W2415594836","https://openalex.org/W2544905596","https://openalex.org/W2617137613","https://openalex.org/W2618530766","https://openalex.org/W2744067593","https://openalex.org/W2772084711","https://openalex.org/W2791384746","https://openalex.org/W2885732902","https://openalex.org/W2889347686","https://openalex.org/W2891408454","https://openalex.org/W2897557170","https://openalex.org/W2908875359","https://openalex.org/W2910660149","https://openalex.org/W2914618626","https://openalex.org/W2947621394","https://openalex.org/W2961350108","https://openalex.org/W2979101700","https://openalex.org/W2992148407","https://openalex.org/W3001566134","https://openalex.org/W3012055358","https://openalex.org/W3014146531","https://openalex.org/W3029002268","https://openalex.org/W3083956363","https://openalex.org/W3126272279","https://openalex.org/W3137613462","https://openalex.org/W3147229768","https://openalex.org/W3173407600","https://openalex.org/W3177862163","https://openalex.org/W3208991442","https://openalex.org/W3215567741","https://openalex.org/W3216106896","https://openalex.org/W3217173838","https://openalex.org/W4210562913","https://openalex.org/W4283750241","https://openalex.org/W4293065977","https://openalex.org/W4361855816","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W2362167128","https://openalex.org/W132219086","https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W2142795561","https://openalex.org/W4205302943","https://openalex.org/W1963862887","https://openalex.org/W2561132942","https://openalex.org/W4360585189"],"abstract_inverted_index":{"Remaining":[0],"Useful":[1],"Life":[2],"(RUL)":[3],"estimation":[4,36,79],"is":[5,75,95],"a":[6],"crucial":[7],"technology":[8],"in":[9,29,41,170],"prognostic":[10],"and":[11,23,54,103,118,126,133,167,172],"health":[12],"management":[13],"(PHM)":[14],"for":[15,34,68,77],"modern":[16],"aero-engines,":[17],"as":[18],"it":[19],"ensures":[20],"the":[21,52,56,86,116,124,129,136,144,153,159],"reliability":[22],"safety":[24],"of":[25,58,88,128,135],"aircraft.":[26],"With":[27],"advances":[28],"sensor":[30,90],"technology,":[31],"data-driven":[32],"approaches":[33],"RUL":[35,59,70,78],"have":[37],"gained":[38],"significant":[39],"interest":[40],"recent":[42],"years,":[43],"especially":[44],"deep":[45],"learning-based":[46],"methods.":[47],"To":[48],"further":[49],"contribute":[50],"to":[51,108,165],"field":[53],"improve":[55],"accuracy":[57],"estimation,":[60],"this":[61],"paper,":[62],"proposes":[63],"novel":[64],"Transformer-based":[65],"fusion":[66,156],"models":[67,138],"aero-engine":[69,89],"estimation.":[71],"The":[72,92,131,148],"vanilla":[73],"Transformer":[74,94],"adapted":[76],"by":[80],"modifying":[81],"its":[82],"structure":[83],"based":[84],"on":[85,143],"characteristics":[87],"data.":[91],"modified":[93],"then":[96],"fused":[97],"with":[98,163],"Long":[99],"Short-Term":[100],"Memory":[101],"(LSTM)":[102],"Convolutional":[104],"Neural":[105],"Network":[106],"(CNN)":[107],"extract":[109],"degradation":[110],"features":[111],"from":[112],"multiple":[113],"aspects.":[114],"Specifically,":[115],"LSTM":[117],"CNN":[119],"layers":[120],"are":[121,139],"incorporated":[122],"into":[123],"decoder":[125],"encoder":[127],"Transformer.":[130],"effectiveness":[132],"superiority":[134],"proposed":[137,154],"demonstrated":[140],"through":[141],"experiments":[142],"C-MAPSS":[145],"benchmark":[146],"dataset.":[147],"experimental":[149],"results":[150],"show":[151],"that":[152],"LSTM-Transformer":[155],"model":[157],"outperforms":[158],"existing":[160],"state-of-the-art":[161],"approaches,":[162],"up":[164],"66.53%":[166],"84.86%":[168],"improvement":[169],"RMSE":[171],"score":[173],"metrics,":[174],"respectively.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}