کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6458593 | 1421108 | 2017 | 10 صفحه PDF | دانلود رایگان |
- The ISO 11783 (ISOBUS) interface is used for data acquisition on agricultural machines.
- Machine status is derived by combining machine data, spatial data and infrastructure data.
- Machine status is combined with a time classification to extract machine operation profiles.
- A simple data logging system and a high degree of automation allow analysis of big machine fleets.
An operation profile is a detailed description of machinery use and provides information about the production process (e.g. time and energy requirements). The high complexity of agricultural machine use over time with its various implements and production processes makes it difficult to automate the operation profile generation for agricultural machinery. Today, modern communication interfaces, like the ISO 11783 (ISOBUS), allow a comfortable data acquisition with comprehensive parameter information of the machine and production process status. This paper describes how the generation of operation profiles can be automated and what kind of machine data and status information are required. Experiments were conducted with a tractor-machine combination during field cultivation. The machine was equipped with a CAN data logger to record ISOBUS messages and GNSS position data during normal operation. A software tool was setup and programmed to drive the data logger. Fields were automatically detected by algorithms to avoid the time consuming manual definition of field boundaries. A graphical user interface simplified the setup and use of the software application. Furthermore, the algorithms analyzed the machine communication regarding the actual machine state based on position, machine activity and work state. The KTBL time classification 2013 was used as a basis for working time analysis as it is designed to divide the overall operational time of agricultural machines into clearly defined, qualitatively different partial time fractions. The machine state could be visualized in the software by a map to show the user where problems occurred e.g. in time losses while waiting or machine breakdown. A diagram presented the timespans spent in different time fractions for comparison to other, similar operations. Combined with other infrastructure data, specific operation profiles could be used to gain more detailed knowledge about machine use or to improve future operations.
Journal: Computers and Electronics in Agriculture - Volume 140, August 2017, Pages 227-236