Application of Artificial Intelligence Methods to Modeling of Injector Needle Movement in Diesel Engine

The paper presents analysis of injector needle movement in the diesel engine. A methodology for obtaining models of this movement was described. The values of injector needle lift were recorded for the engine running at full load, powered by diesel fuel. The models were built using two computational intelligence methods: genetic-fuzzy system and regression trees. The analysis of transparency and accuracy of the obtained models was conducted. The proposed models can be applied to estimation of the amount of fuel injected during the engine work cycle.

• Record URL:
  https://doi.org/10.1016/j.proeng.2017.02.229
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1877705817307361
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18777058
• Supplemental Notes:
  • © 2017 Michał Kekez et al. Published by Elsevier Ltd.
• Authors:
  • Kekez, Michał
  • Radziszewski, Leszek
  • Sapietova, Alžbeta
• Conference:
  • XXI Polish-Slovak Scientific Conference Machine Modeling and Simulations (MMS 2016)
  • Location: Hucisko , Poland
  • Date: 2016-9-6 to 2016-9-8
• Publication Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 303-306
• Serial:
  • Procedia Engineering
  • Volume: 177
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-7058
  • Serial URL: http://www.sciencedirect.com/science/journal/18777058
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Artificial intelligence; Diesel engines; Fuel injectors; Fuzzy systems; Genetic algorithms; Mathematical models; Regression analysis; Trees (Mathematics)
• Uncontrolled Terms: Injector needle movement
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01632313
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 15 2017 6:45PM