Evaluation of combine harvester parameters using manual and auto-steering modes
2021
Juostas, A., Vytautas Magnus Univ., Kaunas (Lithuania) | Jotautiene, E., Vytautas Magnus Univ., Kaunas (Lithuania)
Automatic steering and other automated control systems for technological processes are widely used in modern intelligent farming systems. Automation of technological processes maximizes the technical potential, shortens the time of technological processes, and reduces the fuel consumption. The installed automatic cruise control systems monitor the set parameters and continuously change the combine speed in the fields based on the data received. The choice of the driving speed is influenced by the following controlled parameters, such as grain separation and cleaning losses, the threshing drum speed, engine load, and the height of the grain mass flow in the feeder house conveyor. The automatic control systems can increase the efficiency of combines by up to 10%. The aim of the study is to analyse the combine harvester operation benefits using manual and auto-steering modes. During the grain harvest, the combine operating data parameters were stored in the microprocessor of the remote monitoring system. During the research, analyses of the combine harvester working time structure, fuel consumption, and other operating data were performed. The analysis of experimental data showed that by using the automatic steering function in straight-ahead driving, the working time for technological operations is used more rationally. The structural analysis of the collected working time data of the combine harvester was performed by the methods of mathematical statistics. It was found that the combine used on average 46 ± 5% of the total working time for the technological process. The same analysis of the combine travel time from one field to another, standing due to a full hopper and due to combine failure and other data were analysed. In summary, it can be said that the analysis of the structure of work processes provides detailed information that allows decisions to be made to increase the overall productivity of the machine and to optimize the work processes.
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