{"id":"https://openalex.org/W4407786554","doi":"https://doi.org/10.1109/icmcsi64620.2025.10883526","title":"Predictive Maintenance of Aircraft Engines: Machine Learning Approaches for Remaining Useful Life Estimation","display_name":"Predictive Maintenance of Aircraft Engines: Machine Learning Approaches for Remaining Useful Life Estimation","publication_year":2025,"publication_date":"2025-01-07","ids":{"openalex":"https://openalex.org/W4407786554","doi":"https://doi.org/10.1109/icmcsi64620.2025.10883526"},"language":"en","primary_location":{"id":"doi:10.1109/icmcsi64620.2025.10883526","doi":"https://doi.org/10.1109/icmcsi64620.2025.10883526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmcsi64620.2025.10883526","pdf_url":null,"source":{"id":"https://openalex.org/S7407085903","display_name":"International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5028763949","display_name":"J. Deepika","orcid":"https://orcid.org/0000-0002-1246-4609","observed_orcids":["https://orcid.org/0000-0002-1246-4609"]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepika J","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology, Vellore,Vellore,Tamil Nadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore,Vellore,Tamil Nadu,India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Pappuru Manikanta Reddy","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pappuru Manikanta Reddy","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology, Vellore,Vellore,Tamil Nadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore,Vellore,Tamil Nadu,India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kothaluri Murari","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kothaluri Murari","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology, Vellore,Vellore,Tamil Nadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore,Vellore,Tamil Nadu,India","institution_ids":["https://openalex.org/I876193797"]}]},{"author_position":"last","author":{"id":null,"display_name":"Boyana Rahul","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I876193797","display_name":"Vellore Institute of Technology University","ror":"https://ror.org/00qzypv28","country_code":"IN","type":"education","lineage":["https://openalex.org/I876193797"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Boyana Rahul","raw_affiliation_strings":["School of Computer Science and Engineering, Vellore Institute of Technology, Vellore,Vellore,Tamil Nadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore,Vellore,Tamil Nadu,India","institution_ids":["https://openalex.org/I876193797"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I876193797"],"apc_list":null,"apc_paid":null,"fwci":12.8341,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.99104874,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1679","last_page":"1686"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9926999807357788,"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":0.9926999807357788,"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":0.004999999888241291,"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":0.0007999999797903001,"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/predictive-maintenance","display_name":"predictive maintenance","score":0.9539999961853027},{"id":"https://openalex.org/keywords/aircraft-engines","display_name":"aircraft engines","score":0.9729999899864197},{"id":"https://openalex.org/keywords/remaining-useful-life-prediction","display_name":"remaining useful life prediction","score":0.9390000104904175},{"id":"https://openalex.org/keywords/c-mapss","display_name":"C-MAPSS dataset","score":0.9739999771118164},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.9350000023841858},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.8109999895095825},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.8629999756813049},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.828000009059906},{"id":"https://openalex.org/keywords/engine-health-monitoring","display_name":"engine condition monitoring","score":0.5109999775886536},{"id":"https://openalex.org/keywords/real-time-visualization","display_name":"real-time visualization","score":0.5879999995231628},{"id":"https://openalex.org/keywords/sensor-data","display_name":"sensor data","score":0.6669999957084656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6254966259002686},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.5537513494491577},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5144014954566956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4374655783176422},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.4307501018047333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36078697443008423},{"id":"https://openalex.org/C178802073","wikidata":"https://www.wikidata.org/wiki/Q8421","display_name":"Aeronautics","level":1,"score":0.34443914890289307},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.31632721424102783},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.17476439476013184}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmcsi64620.2025.10883526","doi":"https://doi.org/10.1109/icmcsi64620.2025.10883526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmcsi64620.2025.10883526","pdf_url":null,"source":{"id":"https://openalex.org/S7407085903","display_name":"International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5378999710083008}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2102738861","https://openalex.org/W2120841219","https://openalex.org/W2544905596","https://openalex.org/W2794987257","https://openalex.org/W2810292802","https://openalex.org/W2885732902","https://openalex.org/W2887044785","https://openalex.org/W2902700103","https://openalex.org/W3128690092","https://openalex.org/W3160740916","https://openalex.org/W4200209943","https://openalex.org/W4220742834","https://openalex.org/W4310024794","https://openalex.org/W4312958028","https://openalex.org/W4377020175","https://openalex.org/W4381434049","https://openalex.org/W4402197569"],"related_works":["https://openalex.org/W4408145650","https://openalex.org/W4405793147","https://openalex.org/W7126159487","https://openalex.org/W4400283382","https://openalex.org/W4404658859","https://openalex.org/W7172557328","https://openalex.org/W7219601383","https://openalex.org/W4387075022","https://openalex.org/W4407976456","https://openalex.org/W4405363077"],"abstract_inverted_index":{"In":[0],"the":[1,55,71,123,135,153,184,189],"realm":[2],"of":[3,14,43,57,141,147,193],"predictive":[4,197],"maintenance":[5],"for":[6,67,84,90,225],"aircraft":[7],"engines,":[8],"ensuring":[9],"precise":[10],"and":[11,26,41,64,88,113,119,130,143,181,191,203,221,227],"timely":[12],"predictions":[13],"Remaining":[15],"Useful":[16],"Life":[17],"(RUL)":[18],"is":[19],"paramount":[20],"to":[21,101,206,214],"enhancing":[22,179],"safety,":[23],"reducing":[24],"costs,":[25],"minimizing":[27],"downtime.":[28],"While":[29],"deep":[30,158],"learning":[31,59,159,195],"models":[32,60,124,160],"dominate":[33],"this":[34],"field,":[35],"their":[36],"complexity,":[37],"high":[38],"computational":[39,163],"demand,":[40],"lack":[42],"interpretability":[44,180,222],"can":[45],"hinder":[46],"practical":[47],"deployment":[48],"in":[49,183,196],"real-world":[50],"scenarios.":[51],"This":[52,186],"study":[53],"demonstrates":[54],"potential":[56,213],"machine":[58,194],"as":[61,134],"an":[62,139,144],"efficient":[63],"interpretable":[65],"alternative":[66],"RUL":[68,207],"prediction,":[69],"leveraging":[70],"publicly":[72],"available":[73],"C-MAPSS":[74,79],"dataset":[75],"from":[76,98],"NASA.":[77],"Using":[78],"dataset,":[80],"we":[81],"integrated":[82],"PostgreSQL":[83],"scalable":[85],"data":[86,99],"storage":[87],"Flask":[89],"interactive":[91],"real-time":[92],"visualization,":[93],"creating":[94],"a":[95,117,200],"seamless":[96],"pipeline":[97],"ingestion":[100],"result":[102],"interpretation.":[103],"Preprocessing":[104],"steps,":[105],"including":[106],"normalization,":[107],"noise":[108],"removal":[109],"via":[110],"box":[111],"plots,":[112],"feature":[114],"engineering,":[115],"ensured":[116],"clean":[118],"reliable":[120],"dataset.":[121],"Among":[122],"evaluated-XGBoost,":[125],"Random":[126],"Forest,":[127],"SVM,":[128],"KNN,":[129],"Linear":[131],"Regression-XGBoost":[132],"emerged":[133],"best":[136],"performer,":[137],"achieving":[138],"RMSE":[140],"23.8":[142],"R2":[145],"score":[146],"0.67.":[148],"These":[149],"results":[150],"nearly":[151],"matches":[152],"accuracy":[154],"typically":[155],"associated":[156],"with":[157],"while":[161],"maintaining":[162],"efficiency.":[164,229],"Feature":[165],"importance":[166],"analysis":[167],"through":[168],"Explainable":[169],"AI":[170],"(XAI)":[171],"further":[172],"revealed":[173],"critical":[174,224],"insights":[175],"into":[176],"sensor":[177],"contributions,":[178],"trust":[182],"predictions.":[185],"work":[187],"underscores":[188],"practicality":[190],"effectiveness":[192],"maintenance,":[198],"offering":[199],"robust,":[201],"transparent,":[202],"resource-efficient":[204],"approach":[205],"prediction.":[208],"The":[209],"findings":[210],"highlight":[211],"its":[212],"meet":[215],"industry":[216],"needs":[217],"where":[218],"simplicity,":[219],"accuracy,":[220],"are":[223],"decision-making":[226],"operational":[228]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":7}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-02-21T00:00:00"}
