{"id":"https://openalex.org/W4382998740","doi":"https://doi.org/10.1109/tim.2023.3291733","title":"Time-Varying Gaussian Encoder-Based Adaptive Sensor-Weighted Method for Turbofan Engine Remaining Useful Life Prediction","display_name":"Time-Varying Gaussian Encoder-Based Adaptive Sensor-Weighted Method for Turbofan Engine Remaining Useful Life Prediction","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4382998740","doi":"https://doi.org/10.1109/tim.2023.3291733"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3291733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3291733","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/A5028426177","display_name":"Lei Ren","orcid":"https://orcid.org/0000-0001-6346-6930"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Ren","raw_affiliation_strings":["School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","Zhongguancun Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6346-6930","affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Zhongguancun Laboratory, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062426863","display_name":"Haiteng Wang","orcid":"https://orcid.org/0000-0002-7316-3607"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiteng Wang","raw_affiliation_strings":["School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7316-3607","affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017044026","display_name":"Zidi Jia","orcid":"https://orcid.org/0000-0002-3746-7742"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zidi Jia","raw_affiliation_strings":["School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3746-7742","affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017308450","display_name":"Yuanjun Laili","orcid":"https://orcid.org/0000-0003-3834-4602"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanjun Laili","raw_affiliation_strings":["School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","Zhongguancun Laboratory, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3834-4602","affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"Zhongguancun Laboratory, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100418969","display_name":"Zhang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Zhang","raw_affiliation_strings":["School of Automation Science and Electrical Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1989-6102","affiliations":[{"raw_affiliation_string":"School of Automation Science and Electrical Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5028426177"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":1.6141,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.83262873,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9940999746322632,"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":0.9940999746322632,"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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":0.979200005531311,"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/turbofan","display_name":"Turbofan","score":0.9743033647537231},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5686726570129395},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5453897714614868},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5176573395729065},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4797321856021881},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.47756487131118774},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47722747921943665},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47469180822372437},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4639536738395691},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44164878129959106},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42859819531440735},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.41996896266937256},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35645607113838196},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.31458455324172974},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.19384464621543884},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09247234463691711}],"concepts":[{"id":"https://openalex.org/C110050840","wikidata":"https://www.wikidata.org/wiki/Q654051","display_name":"Turbofan","level":2,"score":0.9743033647537231},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5686726570129395},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5453897714614868},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5176573395729065},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4797321856021881},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.47756487131118774},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47722747921943665},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47469180822372437},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4639536738395691},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44164878129959106},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42859819531440735},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.41996896266937256},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35645607113838196},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.31458455324172974},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.19384464621543884},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09247234463691711},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/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},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3291733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3291733","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":[{"id":"https://metadata.un.org/sdg/12","score":0.5,"display_name":"Responsible consumption and production"}],"awards":[{"id":"https://openalex.org/G3160825066","display_name":null,"funder_award_id":"62173023","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6608322027","display_name":null,"funder_award_id":"92167108","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7800621295","display_name":null,"funder_award_id":"62225302","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":34,"referenced_works":["https://openalex.org/W2053443947","https://openalex.org/W2095705004","https://openalex.org/W2187089797","https://openalex.org/W2323403147","https://openalex.org/W2468885078","https://openalex.org/W2727078075","https://openalex.org/W2744067593","https://openalex.org/W2767630563","https://openalex.org/W2772084711","https://openalex.org/W2910660149","https://openalex.org/W2978540646","https://openalex.org/W3006585575","https://openalex.org/W3014146531","https://openalex.org/W3033009913","https://openalex.org/W3037944824","https://openalex.org/W3037995823","https://openalex.org/W3040694753","https://openalex.org/W3044911604","https://openalex.org/W3082015310","https://openalex.org/W3093879950","https://openalex.org/W3130218378","https://openalex.org/W3132280229","https://openalex.org/W3188219698","https://openalex.org/W3194964617","https://openalex.org/W4206652641","https://openalex.org/W4210562913","https://openalex.org/W4210745385","https://openalex.org/W4221023673","https://openalex.org/W4225463562","https://openalex.org/W4226069359","https://openalex.org/W4285187956","https://openalex.org/W4360770771","https://openalex.org/W6674330103","https://openalex.org/W6746338567"],"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/W2370335777"],"abstract_inverted_index":{"Remaining":[0],"useful":[1],"life":[2],"(RUL)":[3],"prediction,":[4],"as":[5],"an":[6,119],"essential":[7],"aspect":[8],"of":[9,27,70,115,170,174],"condition-based":[10],"maintenance,":[11],"has":[12],"attracted":[13],"substantial":[14],"interest":[15],"in":[16,177],"industrial":[17,178],"measurement.":[18,179],"Recently,":[19],"deep":[20],"learning-based":[21],"methods":[22,166],"have":[23,39],"achieved":[24],"superior":[25,162],"performance":[26,163],"turbofan":[28,80,157],"engine":[29,35,72,81,158],"RUL":[30],"prediction.":[31],"However,":[32],"since":[33],"the":[34,53,68,112,152,161,168],"multi-sensor":[36,82,116,175],"raw":[37],"signals":[38,83,133,176],"noise":[40],"and":[41,55,65,84,109,131,167,172],"complex":[42],"operation":[43],"conditions,":[44],"constructing":[45],"representative":[46],"features":[47],"is":[48,104,123,143],"challenging,":[49],"which":[50],"severely":[51],"impacts":[52],"accuracy":[54],"generalization.":[56],"Moreover,":[57],"existing":[58],"approaches":[59],"tend":[60],"to":[61,95,106,110,126,145,150],"extract":[62],"temporal":[63],"dependencies":[64],"ignore":[66],"identifying":[67],"contribution":[69],"different":[71],"sensors.":[73],"In":[74],"this":[75],"paper,":[76],"we":[77],"focus":[78],"on":[79,135,156],"propose":[85],"a":[86,100,139],"Time-varying":[87],"Gaussian":[88,102],"Encoder":[89],"based":[90],"Adaptive":[91],"Sensor-Weighted":[92],"method":[93],"(TGE-ASW)":[94],"alleviate":[96],"these":[97],"problems.":[98],"First,":[99],"time-varying":[101],"encoder":[103],"built":[105,144],"enhance":[107],"generalization":[108],"stabilize":[111],"training":[113],"process":[114],"signals.":[117],"Then,":[118],"adaptive":[120,127],"sensor-weighted":[121],"strategy":[122],"carried":[124],"out":[125],"identify":[128],"important":[129],"sensors":[130],"weight":[132],"conditioned":[134],"each":[136],"sample.":[137],"Finally,":[138],"convolutional":[140],"neural":[141],"network":[142],"obtain":[146],"high-level":[147],"feature":[148],"representation":[149],"predict":[151],"RUL.":[153],"Experimental":[154],"results":[155],"datasets":[159],"demonstrate":[160],"over":[164],"state-of-the-art":[165],"effectiveness":[169],"processing":[171],"representing":[173]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}