Comparative evaluation of machine learning models for predicting noise and vibration of a biodiesel-CNG fuelled diesel engine

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dc.contributor.authorUluocak, Ihsan
dc.contributor.authorUludamar, Erinc
dc.date.accessioned2026-02-27T07:33:01Z
dc.date.available2026-02-27T07:33:01Z
dc.date.issued2025
dc.description.abstractImproving engine operation through the implementation of intelligent modelling is crucial for reducing vibration and noise. For this reason, In the present study, advanced machine learning models including Radial Basis Function Neural Network (RBFNN), General Regression Neural Network (GRNN), Support Vector Machine (SVM), and ensemble models with Least Squares Boosting (LSboost) are employed to predict noise and vibration of a diesel engine. The engine is fuelled with low-sulphur diesel, sunflower biodiesel-diesel blends at 20 % and 40 % by volume and compressed natural gas (CNG) added through the intake manifold at various flow rates. Noise and vibration data were gathered at intervals of 300 rpm between 1200 rpm and 2400 rpm. Results show that the among proposed models, for noise predictions, GRNN yield the best results among all models with R2 accuracy of 0.9983 and Theil U2 of 0.073. Meanwhile, in the lights of vibration results, RBFNN outperforms other models with R2 accuracy of 0.9968 and Theil U2 of 0.214.
dc.identifier.doi10.1016/j.measurement.2025.117021
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.urihttp://dx.doi.org/10.1016/j.measurement.2025.117021
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4422
dc.identifier.volume249
dc.identifier.wosWOS:001428764200001
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofMeasurement
dc.relation.publicationcategoryMakale - Uluslararas� Hakemli Dergi - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20260302
dc.subjectVibration
dc.subjectNoise
dc.subjectCNG
dc.subjectRBFNN
dc.subjectLSBoost
dc.subjectBiodiesel
dc.titleComparative evaluation of machine learning models for predicting noise and vibration of a biodiesel-CNG fuelled diesel engine
dc.typeArticle

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