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dc.contributor.authorŽvirblis, Tadas,
dc.contributor.authorHunicz, Jacek,
dc.contributor.authorMatijošius, Jonas,
dc.contributor.authorRimkus, Alfredas,
dc.contributor.authorKilikevičius, Artūras,
dc.contributor.authorGęca, Michał,
dc.date.accessioned2023-12-22T07:05:47Z
dc.date.available2023-12-22T07:05:47Z
dc.date.issued2023.
dc.identifier.issn1507-2711
dc.identifier.other(crossref_id)154597893
dc.identifier.urihttps://etalpykla.vilniustech.lt/xmlui/handle/123456789/153521
dc.description.abstractThe reliability of internal combustion engines becomes an important aspect when traditional fuels with biofuels. Therefore, the development of prognostic models becomes very important for evaluating and predicting the replacement of traditional fuels with biofuels in internal combustion engines. The models have been made to model AVL 5402 engine emission, vibration, and sound pressure parameters using a three-stage statistical regression models. The fifteen parameters might be accurately predicted by a single statistic presented here. Both fuel type (diesel fuel and HVO) and engine parameters that can be adjusted were considered, since this analysis followed the symmetry of the methods. The data analysis process included three distinct steps and symmetric statistical regression testing was performed. The algorithm examined the effectiveness of various engine settings. Finally, the optimal fixed engine parameter and the optimal statistic were used to construct an ANCOVA model. The ANCOVA model improved the accuracy of prediction for all fifteen missing parameters.eng
dc.formatPDF
dc.format.extentp. 1-19.
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyDOAJ
dc.rightsLaisvai prieinamas internete.
dc.source.urihttps://ein.org.pl/pdf-174358-96044?filename=Improving%20Diesel%20Engine.pdf
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:182285323/datastreams/MAIN/content
dc.titleImproving diesel engine reliability using an optimal prognostic model to predict diesel engine emissions and performance using pure diesel and hydrogenated vegetable oil /
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.accessRightsThis is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references47
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionVilniaus universitetas
dc.contributor.institutionLublin University of Technology
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyMechanikos fakultetas / Faculty of Mechanics
dc.contributor.facultyTransporto inžinerijos fakultetas / Faculty of Transport Engineering
dc.contributor.departmentMechanikos mokslo institutas / Institute of Mechanical Science
dc.subject.researchfieldT 009 - Mechanikos inžinerija / Mechanical enginering
dc.subject.vgtuprioritizedfieldsMC0101 - Mechatroninės gamybos sistemos Pramonė 4.0 platformoje / Mechatronic for Industry 4.0 Production System
dc.subject.ltspecializationsL104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies
dc.subject.enANCOVA
dc.subject.enhydrotreated vegetable oil
dc.subject.enMAPE
dc.subject.enStatistical regression analysis
dc.subject.enlinear regression models
dc.subject.enEngine’s reliability
dcterms.sourcetitleEksploatacja i niezawodność – Maintenance and reliability.
dc.description.issueiss. 4
dc.description.volumevol. 25
dc.publisher.namePolish Maintenance Society
dc.publisher.cityWarsaw
dc.identifier.doi154597893
dc.identifier.doi001098415300001
dc.identifier.doi2-s2.0-85176499246
dc.identifier.doi10.17531/ein/174358
dc.identifier.elaba182285323


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