Parametric Characterization of a Tractor Engine by Specific Fuel Consumption

Abstract

The paper highlights that equipping agricultural mobile machines with sensors and electronic controls enables the remote acquisition of real-time information regarding the technical condition of engine systems while operation. (Research purpose) The objective of this research is to formulate a methodology for determining the multi-parameter characteristics of specific effective fuel consumption. This is illustrated through an examination of the Deutz BF 6M 2012 C engine, installed in the of the Terrion ATM 4200 tractor. (Materials and methods) The electronic control system was examined through data analysis. Utilizing Logic Analyzer 8, a connected logic analyzer, facilitated the extraction of a multi-parameter characteristic related to the specific fuel consumption of the engine. This data was obtained from the CAN bus while the machine was in operation. A statistical data processing method was developed using the Statistica 10 program. Regression equations were formulated to illustrate the correlation between fuel consumption, crankshaft speed and engine torque. The statistical significance of the relationship between fuel consumption across the entire range of operating modes and the selected parameters was corroborated by the values of the coefficient of determination and Fisher’s criterion. (Results and discussion) The data from the multi-parameter characteristic, illustrating the correlation between fuel consumption, engine speed, and torque, aligns with the information provided by the manufacturing plant. This alignment further validates the accuracy of the derived regression equations. (Conclusions) The suggested sequence of steps for obtain a multi-parameter characteristic can be applied to other engine performance indicators. Monitoring operational performance to analyze information on the technical condition of machine components and assemblies is necessary for diagnostics and ensuring timely maintenance and repair.

Keywords

• intelligent system
• technical condition monitoring
• engine system monitoring
• performance indicators
• technical diagnostics