{"id":"https://openalex.org/W7128917709","doi":"https://doi.org/10.3390/machines14020226","title":"State-Space Estimation in Discriminant Subspace: A Kalman Filtering Approach for Turbofan Engine RUL Prediction","display_name":"State-Space Estimation in Discriminant Subspace: A Kalman Filtering Approach for Turbofan Engine RUL Prediction","publication_year":2026,"publication_date":"2026-02-14","ids":{"openalex":"https://openalex.org/W7128917709","doi":"https://doi.org/10.3390/machines14020226"},"language":"en","primary_location":{"id":"doi:10.3390/machines14020226","doi":"https://doi.org/10.3390/machines14020226","is_oa":true,"landing_page_url":"https://doi.org/10.3390/machines14020226","pdf_url":"https://www.mdpi.com/2075-1702/14/2/226/pdf?version=1771061348","source":{"id":"https://openalex.org/S4210219839","display_name":"Machines","issn_l":"2075-1702","issn":["2075-1702"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machines","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2075-1702/14/2/226/pdf?version=1771061348","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115620491","display_name":"U\u011fur Y\u0131ld\u0131r\u0131m","orcid":"https://orcid.org/0000-0003-1131-8893","observed_orcids":["https://orcid.org/0000-0003-1131-8893"]},"institutions":[{"id":"https://openalex.org/I50379099","display_name":"Adana Science and Technology University","ror":"https://ror.org/013z3yn41","country_code":"TR","type":"education","lineage":["https://openalex.org/I50379099"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"U\u011fur Y\u0131ld\u0131r\u0131m","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Faculty of Engineering, Adana Alparslan T\u00fcrke\u015f Science and Technology University, Adana 01250, T\u00fcrkiye"],"raw_orcid":"https://orcid.org/0000-0003-1131-8893","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Faculty of Engineering, Adana Alparslan T\u00fcrke\u015f Science and Technology University, Adana 01250, T\u00fcrkiye","institution_ids":["https://openalex.org/I50379099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042618561","display_name":"H\u00fcseyin Af\u015fer","orcid":"https://orcid.org/0000-0002-6302-4558","observed_orcids":["https://orcid.org/0000-0002-6302-4558"]},"institutions":[{"id":"https://openalex.org/I50379099","display_name":"Adana Science and Technology University","ror":"https://ror.org/013z3yn41","country_code":"TR","type":"education","lineage":["https://openalex.org/I50379099"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"H\u00fcseyin Af\u0219er","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, Faculty of Engineering, Adana Alparslan T\u00fcrke\u015f Science and Technology University, Adana 01250, T\u00fcrkiye"],"raw_orcid":"https://orcid.org/0000-0002-6302-4558","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Faculty of Engineering, Adana Alparslan T\u00fcrke\u015f Science and Technology University, Adana 01250, T\u00fcrkiye","institution_ids":["https://openalex.org/I50379099"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5115620491"],"corresponding_institution_ids":["https://openalex.org/I50379099"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2896},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2896},"fwci":2.8442,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.869913,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"14","issue":"2","first_page":"226","last_page":"226"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9663000106811523,"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":0.9663000106811523,"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":0.013399999588727951,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.010300000198185444,"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/remaining-useful-life-prediction","display_name":"remaining useful life prediction","score":0.9309999942779541},{"id":"https://openalex.org/keywords/turbofan-engine","display_name":"turbofan engine","score":0.8529999852180481},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"linear discriminant analysis","score":0.8489999771118164},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.7839999794960022},{"id":"https://openalex.org/keywords/unscented-kalman-filter","display_name":"unscented Kalman filter","score":0.972000002861023},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"extended Kalman filter","score":0.9929999709129333},{"id":"https://openalex.org/keywords/linear-kalman-filter","display_name":"linear Kalman filter","score":0.9210000038146973},{"id":"https://openalex.org/keywords/state-space-estimation","display_name":"state-space estimation","score":0.7710000276565552},{"id":"https://openalex.org/keywords/nasa-c-mapss","display_name":"NASA C-MAPSS","score":0.746999979019165},{"id":"https://openalex.org/keywords/interpretability","display_name":"interpretability","score":0.5019999742507935},{"id":"https://openalex.org/keywords/real-time-health-monitoring","display_name":"real-time health monitoring","score":0.5680000185966492}],"concepts":[{"id":"https://openalex.org/C110050840","wikidata":"https://www.wikidata.org/wiki/Q654051","display_name":"Turbofan","level":2,"score":0.890500009059906},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.7684999704360962},{"id":"https://openalex.org/C129364497","wikidata":"https://www.wikidata.org/wiki/Q3042561","display_name":"Prognostics","level":2,"score":0.7549999952316284},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.6524999737739563},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6503999829292297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47679999470710754},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.42410001158714294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42170000076293945},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3944999873638153},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C139399703","wikidata":"https://www.wikidata.org/wiki/Q7897426","display_name":"Unscented transform","level":5,"score":0.34060001373291016},{"id":"https://openalex.org/C150679823","wikidata":"https://www.wikidata.org/wiki/Q5436946","display_name":"Fast Kalman filter","level":4,"score":0.3398999869823456},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C8639503","wikidata":"https://www.wikidata.org/wiki/Q6059511","display_name":"Invariant extended Kalman filter","level":4,"score":0.3005000054836273},{"id":"https://openalex.org/C167740415","wikidata":"https://www.wikidata.org/wiki/Q2876213","display_name":"Aerospace","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C39643299","wikidata":"https://www.wikidata.org/wiki/Q178777","display_name":"Jet engine","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2531000077724457},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/machines14020226","doi":"https://doi.org/10.3390/machines14020226","is_oa":true,"landing_page_url":"https://doi.org/10.3390/machines14020226","pdf_url":"https://www.mdpi.com/2075-1702/14/2/226/pdf?version=1771061348","source":{"id":"https://openalex.org/S4210219839","display_name":"Machines","issn_l":"2075-1702","issn":["2075-1702"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machines","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:68246f0856c1415abe2aaf5c3801b5f1","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/68246f0856c1415abe2aaf5c3801b5f1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machines, Vol 14, Iss 2, p 226 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/machines14020226","doi":"https://doi.org/10.3390/machines14020226","is_oa":true,"landing_page_url":"https://doi.org/10.3390/machines14020226","pdf_url":"https://www.mdpi.com/2075-1702/14/2/226/pdf?version=1771061348","source":{"id":"https://openalex.org/S4210219839","display_name":"Machines","issn_l":"2075-1702","issn":["2075-1702"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machines","raw_type":"journal-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"score":0.5866596698760986,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7128917709.pdf","grobid_xml":"https://content.openalex.org/works/W7128917709.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"remaining":[1],"useful":[2],"life":[3],"(RUL)":[4],"prediction":[5],"of":[6,137],"turbofan":[7,43],"engines":[8],"is":[9],"critical":[10,73,172],"for":[11,42,173,211],"aviation":[12],"safety":[13],"and":[14,24,90,103,122,203],"maintenance":[15,175],"optimization;":[16],"however,":[17],"deep":[18,208],"learning":[19,209],"approaches":[20],"often":[21],"lack":[22],"interpretability":[23],"require":[25],"extensive":[26],"training":[27],"data.":[28],"This":[29],"study":[30],"proposes":[31],"a":[32,53,135,140,200],"framework":[33,198],"integrating":[34],"Linear":[35],"Discriminant":[36],"Analysis":[37],"(LDA)":[38],"with":[39,65,134,182],"Kalman":[40,93,96,100,105,161],"filtering":[41],"engine":[44,82,213],"prognostics.":[45],"The":[46,195],"methodology":[47],"projects":[48],"high-dimensional":[49],"sensor":[50],"measurements":[51],"onto":[52],"two-dimensional":[54],"LDA":[55],"subspace,":[56],"where":[57],"degradation":[58],"trajectories":[59],"are":[60],"tracked":[61],"using":[62,114],"state-space":[63],"estimation,":[64],"RUL":[66],"predictions":[67,171],"derived":[68],"from":[69,189],"distances":[70],"to":[71,192,207],"learned":[72],"failure":[74],"boundaries.":[75],"A":[76],"health":[77,214],"index-based":[78],"classification":[79],"scheme":[80],"partitions":[81],"states":[83],"into":[84],"three":[85],"operational":[86],"regions:":[87],"Critical,":[88],"Warning,":[89],"Healthy.":[91],"Three":[92],"filter":[94,162],"variants\u2014Linear":[95],"Filter":[97,101,106],"(LKF),":[98],"Extended":[99],"(EKF),":[102],"Unscented":[104],"(UKF)\u2014were":[107],"compared":[108],"against":[109],"an":[110],"Autoregressive":[111],"(AR)":[112,191],"baseline":[113],"the":[115,120,131,160],"NASA":[116],"C-MAPSS":[117],"dataset.":[118],"Using":[119],"Prognostics":[121],"Health":[123],"Management":[124],"2008":[125],"asymmetric":[126],"scoring":[127],"function,":[128],"UKF":[129],"achieved":[130],"best":[132],"performance":[133],"Score":[136,184],"552572,":[138],"representing":[139],"54.9%":[141],"improvement":[142,181],"over":[143],"AR":[144],"(1224299),":[145],"indicating":[146],"substantially":[147],"fewer":[148],"late":[149,170],"predictions.":[150],"While":[151],"RMSE":[152],"values":[153],"remained":[154],"comparable":[155],"across":[156],"methods":[157,210],"(36\u201337":[158],"cycles),":[159],"variants":[163],"demonstrated":[164,179],"meaningful":[165],"improvements":[166],"in":[167],"avoiding":[168],"dangerous":[169],"safety-oriented":[174],"scheduling.":[176],"EKF":[177],"also":[178],"substantial":[180],"36.1%":[183],"reduction.":[185],"Classification":[186],"accuracy":[187],"improved":[188],"70.72%":[190],"73.27%":[193],"(UKF).":[194],"proposed":[196],"LDA\u2013Kalman":[197],"provides":[199],"computationally":[201],"efficient":[202],"geometrically":[204],"interpretable":[205],"alternative":[206],"real-time":[212],"monitoring.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-08T09:06:37.294493","created_date":"2026-02-15T00:00:00"}