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Improving Diesel Engine Reliability Using an Optimal Prognostic Model to Predict Diesel Engine Emissions and Performance Using Pure Diesel and Hydrogenated Vegetable Oil
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Institute of Data Science and Digital Technologies, Vilnius University, Lithuania
Faculty of Mechanical Engineering, Lublin University of Technology, Poland
Institute of Mechanical science, Faculty of Mechanical Engineering, Vilnius Gediminas Technical University, Vilnius Gediminas Technical University, Lithuania
Department of Automobile Engineering, Vilnius Gediminas Technical University, Lithuania
Submission date: 2023-07-20
Final revision date: 2023-09-14
Acceptance date: 2023-10-20
Online publication date: 2023-11-02
Publication date: 2023-11-02
Corresponding author
Jonas Matijošius   

Institute of Mechanical science, Faculty of Mechanical Engineering, Vilnius Gediminas Technical University, Vilnius Gediminas Technical University, Plytinės str. 25, LT- 10105, Vilnius, Lithuania
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(4):174358
  • A three-step algorithm based on statistical prognostic models was used to implement diesel engine reliability improvements.
  • The creation of a prolific and effective ANCOVA prognostic model.
  • ANCOVA was exceedingly accurate in predicting 95% of the studied parameters.
The 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.
This paper has received funding under postdoctoral fellowship project from the Research Council of Lithuania (LMTLT), agreement No [S PD 22 81]. The authors thank the AVL company for the opportunity to use the engine simulation tool AVL BOOST, which was us ed to analyse the combustion process and present the results. A cooperation agreement has been concluded between the Faculty of the Transport Engineering of Vilnius Tech University and AVL Advanced Simulation Technologies.
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