{"id":"https://openalex.org/W2903571190","doi":"https://doi.org/10.1109/coase.2018.8560579","title":"NARNET-based Prognostics Modeling for Deteriorating Systems under Dynamic Operating Conditions","display_name":"NARNET-based Prognostics Modeling for Deteriorating Systems under Dynamic Operating Conditions","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903571190","doi":"https://doi.org/10.1109/coase.2018.8560579","mag":"2903571190"},"language":"en","primary_location":{"id":"doi:10.1109/coase.2018.8560579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coase.2018.8560579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","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/A5102792594","display_name":"Anqi He","orcid":"https://orcid.org/0000-0003-4254-1422"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anqi He","raw_affiliation_strings":["Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050575763","display_name":"Xiaoning Jin","orcid":"https://orcid.org/0000-0001-9353-8456"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaoning Jin","raw_affiliation_strings":["Department of Mechanical and Industrial engineering, Northeastern University, Boston, MA"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical and Industrial engineering, Northeastern University, Boston, MA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102792594"],"corresponding_institution_ids":["https://openalex.org/I12912129"],"apc_list":null,"apc_paid":null,"fwci":0.5518,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.68821739,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"15","issue":null,"first_page":"1322","last_page":"1327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9980000257492065,"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":0.9980000257492065,"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/T10780","display_name":"Reliability and Maintenance Optimization","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.993399977684021,"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/prognostics","display_name":"Prognostics","score":0.9743430614471436},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6232613325119019},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6059529781341553},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.5061182379722595},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5030621886253357},{"id":"https://openalex.org/keywords/degradation","display_name":"Degradation (telecommunications)","score":0.4677337110042572},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45416221022605896},{"id":"https://openalex.org/keywords/condition-monitoring","display_name":"Condition monitoring","score":0.45173367857933044},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.44996732473373413},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4462411403656006},{"id":"https://openalex.org/keywords/turbine","display_name":"Turbine","score":0.43022122979164124},{"id":"https://openalex.org/keywords/nonlinear-autoregressive-exogenous-model","display_name":"Nonlinear autoregressive exogenous model","score":0.4210261106491089},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.15569904446601868}],"concepts":[{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.9743430614471436},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6232613325119019},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6059529781341553},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.5061182379722595},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5030621886253357},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.4677337110042572},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45416221022605896},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.45173367857933044},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.44996732473373413},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4462411403656006},{"id":"https://openalex.org/C2778449969","wikidata":"https://www.wikidata.org/wiki/Q130760","display_name":"Turbine","level":2,"score":0.43022122979164124},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.4210261106491089},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.15569904446601868},{"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/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/coase.2018.8560579","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coase.2018.8560579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W750621024","https://openalex.org/W1993230770","https://openalex.org/W1994295547","https://openalex.org/W2049169212","https://openalex.org/W2051406062","https://openalex.org/W2098608159","https://openalex.org/W2149956719","https://openalex.org/W2345980206","https://openalex.org/W2594845301","https://openalex.org/W2772084711","https://openalex.org/W2773549135"],"related_works":["https://openalex.org/W2606910468","https://openalex.org/W3116827148","https://openalex.org/W3120843198","https://openalex.org/W2799656149","https://openalex.org/W2154965898","https://openalex.org/W2036704594","https://openalex.org/W4226315710","https://openalex.org/W3083782034","https://openalex.org/W4287185323","https://openalex.org/W2995801509"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,10,24,80,103],"new":[4],"prognostics":[5,117],"modeling":[6],"method":[7],"based":[8,52,106],"on":[9,53,107],"nonlinear":[11],"autoregressive":[12],"neural":[13],"network":[14],"(NARNET)":[15],"for":[16],"computing":[17],"the":[18,46,54,77,87,93,108,116,120,141,145],"remaining":[19],"useful":[20],"life":[21],"(RUL)":[22],"of":[23,34,56,58,79],"deteriorating":[25],"system":[26],"under":[27],"dynamic":[28],"operating":[29,95,98,111,121],"conditions.":[30,96,112],"Our":[31],"approach":[32],"consists":[33],"two":[35],"processes:":[36],"(1)":[37],"an":[38,67],"offline":[39],"training":[40],"process":[41,72,89],"is":[42,73,90,137],"built":[43],"to":[44,75,139],"model":[45,105,118,142,146],"degradation":[47,88,135],"law":[48],"and":[49,70,127,143],"failure":[50],"zones":[51],"dataset":[55,136],"hundreds":[57],"identical":[59],"units":[60],"with":[61],"run-to-failure":[62],"time-series":[63],"sensor":[64],"measurements;":[65],"(2)":[66],"online":[68],"prediction":[69],"testing":[71],"developed":[74],"predict":[76],"RUL":[78,129],"test":[81,144],"unit.":[82],"We":[83,113],"particularly":[84],"investigate":[85],"how":[86],"affected":[91],"by":[92,102],"unit-specific":[94],"The":[97,131],"conditions":[99],"are":[100],"forecasted":[101],"NARNET":[104],"unit's":[109],"historical":[110],"show":[114],"that":[115],"integrating":[119],"condition":[122],"forecast":[123],"provides":[124],"more":[125],"accurate":[126],"efficient":[128],"prediction.":[130],"aircraft":[132],"turbine":[133],"engine":[134],"utilized":[138],"demonstrate":[140],"performance.":[147]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}