Detection Combustion Data Pattern on Gasoline Fuel Motorcycle with Carburetor System

-, Andrizal and Budhi, Bakhtiar and Rivanol , Chadry (2016) Detection Combustion Data Pattern on Gasoline Fuel Motorcycle with Carburetor System. International Journal on Advanced Science, Engineering and Information Technology, Vol. 6 (1). pp. 107-117. ISSN 2088-5334

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Tune up combustion motorcycle engine made in order to obtain a perfect engine combustion category with maximum engine performance and fuel efficiency. Motorcycles with 4-stroke petrol injection system has facilities to process tune up in the form of engine control unit and engine scanner tool. While petrol 4 stroke motorcycle carburetor system is not equipped with facilities such as a motorcycle injection system, consequently, tune up the engine combustion process is done manually. Category of the combustion engine can be determined based on the levels of emissions of HC, CO, CO2 and O2 contained in the exhaust gases of vehicles. This study aims to create a system to detect and display patterns of data categories motorcycle combustion gasoline engine carburetor system through the detection of exhaust emissions. This system is made using four gas sensors are integrated with the system FPGA (Field Progrmable Gate Array) as the main system and a display system using a PC or Laptop. Tests performed on a number of motorcycles with the brand, manufacturer and year of manufacture are different. Detection results shown in graphical form a pattern data categories perfect combustion engine and the data pattern of incomplete combustion category with a response time to 10 seconds. This system is expected to be used as an alternative tool for mechanics in performing tune up combustion motorcycle engine.

Item Type:Article
Subjects:G Technology > GI Electrical engineering. Electronics Nuclear engineering
Divisions:Jurusan Teknik Elektro > Prog.Studi Teknik Elektronika Industri-D4 > Jurnal/Karya Ilmiah
ID Code:17
Deposited By:Mr. OP Repo-4 Puskom
Deposited On:20 Sep 2016 08:37
Last Modified:09 Nov 2016 14:46

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