Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Construction or agricultural-type vehicle
Reexamination Certificate
1999-01-04
2001-03-20
Cuchlinski, Jr., William A. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Construction or agricultural-type vehicle
C701S025000, C172S004500, C172S009000
Reexamination Certificate
active
06205384
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates generally to agricultural machinery, especially self-propelled harvesting machines and, more particularly to a system for setting operating parameters of a harvesting machine.
A harvesting machine, such as a combine harvester, is very complex and comprises a large number of machine components with corresponding parameters, which are set by the machine operator. For example, in a combine harvester these machine parameters may include the threshing drum speed, the blower speed, the screen meshes of lower and upper screens, the reel speed, the traveling speed, etc.
The operator of the harvesting machine must set the parameters of the machine components correctly in order to optimally carry out the harvesting operation. There are several different target standards for an optimum harvesting application. For example, one target standard for the harvesting application relates to minimizing losses to the greatest extent possible. On the other hand, in view of the significant time constraints during the short harvesting period, another target standard is to achieve a high throughput or high surface area capacity and thereby harvesting in as short a time as possible. However, the individual target standards are not independent of one another. Thus, for example, there is a correlation between the throughput and the loss. With increasing throughput, there is generally a concurrent increase in loss. Thus the target standard usually is dictated by a compromise between low losses and rapid completion of the harvesting application.
An added difficulty for the machine operator in setting a self-propelled harvesting machine is that, from one harvesting application to the next, there are in each case different external harvesting conditions which require different settings of the parameters of the machine components. For example, it is possible to harvest different crops (wheat, rye, oats, maize, etc.), which may require different operating parameters of the harvesting machine. Other examples of external harvesting conditions are the ripeness of the crop, the grain moisture, the proportion of straw, the straw moisture, the grain size, stand density (yield), etc.
Experienced machine operators who know the effect of external harvesting conditions on the setting of the machine parameters usually succeed in determining the optimal machine settings. For inexperienced operators, however, it is relatively difficult to set the many machine parameters optimally. Even experienced machine operators have problems in setting the harvesting machine optimally at the beginning of the harvesting season.
A system for setting a harvesting machine is proposed in European Patent Application 0 586 99 A2, which is based on a neuronal network. There the external harvesting conditions are detected by sensors and fed to the input neurons of the input layer as signals. The neuronal network serves its concealed layers for information processing and is designed both as a general model of the harvesting machine and as a local model of the machine with respect to individual machine components. The neurons of the output layer in this system then generate the signals for the machine parameters. The realization of such a system for automatically setting a self-propelled harvesting machine based on a neuronal network however requires a very large computing capacity and fast processors. Moreover high expenditure on programming is necessary. These reasons as well as other associated relatively high costs have precluded implementation of a neuronal network in harvesting machines.
It is therefore the object of the invention to provide a relatively inexpensive system which is easy to implement and which allows optimum setting of a self-propelled harvesting machine.
SUMMARY OF THE INVENTION
In accordance with the present invention there is provided a self-propelled harvesting machine for harvesting crops and having one or more machine components with respectively adjustable parameters and taking into account external harvesting conditions; and comprising a system including means for receiving input about external harvesting conditions for respective particular harvesting applications, storage means for storing at least one machine parameter for different external harvesting conditions, and means for accessing the stored machine parameter from the storage means after the input about the respective external harvesting conditions.
Here the stored machine parameters can be called up from the storage means to set the harvesting machine after the system is familiar with the external harvesting conditions.
To arrive at the optimum values for the machine parameters to be stored, the empirical values of good machine operators can be used as guidelines or benchmarks. In the process it is important to determine which machine parameters have been set by them in given external harvesting conditions. Alternatively or additionally the machine parameters to be stored can also be generated by series of measurements and from known correlations.
In a particular embodiment of the invention the system is expanded by an input option of a target standard. As a result, the machine settings can be further optimized and adapted to the respective machine operator.
The system according to the invention can be implemented in harvesting machines in a simple and inexpensive manner without having to emulate the correlations in the harvesting machine according to program by an elaborate system such as a neuronal network for example. The system according to the invention can be mounted on the electronic hardware platform already existing in the harvesting machine (e.g. CEBIS the electronic on-board information system of the firm Claas KGaA of Harsewinkel, Germany), in contrast to the substantially more elaborate and expensive computer hardware or software is necessary for the implementation of a neuronal network. A microcontroller for automation tasks in agriculture is disclosed in pages 92-95 of papers delivered at the International Conference on Agricultural Engineering held in Berlin, Germany, Oct. 24-26, 1990, and which is hereby incorporated by reference.
According to another feature of the present invention there is provided a method of operating a self-propelled harvesting machine which has machine components with respectively adjustable parameters and taking into account external harvesting conditions, including the steps of: inputting the values for the external harvesting conditions into a computer; selecting a target standard for the harvesting application; computing a configuration set of machine parameters which matches the harvesting conditions and the selected target standard; and operating an actuator to adjust the parameter of the machine component correlative to the computed parameter.
The system and method according to the invention ensures optimum setting of the harvesting machine, even with an inexperienced machine operator. Furthermore, the harvesting machine is not operated with a faulty setting well below its capacity. However, for the even experienced machine operator, who is slightly out of practice at the beginning of the harvesting season, the system is certainly advantageous.
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Copy of article “Gesteuerte adaptive Regelung einer Mähdrescher-Reiningungsanlage” published in “Grundlagen der Landtechnick” Magazine, Band 36 (1986) Nr. 3, pp. 73-78.
Standardized Microcontroller for Automation Tasks in Agriculture, N. Diekhans, Berlin, Oct. 24-26, 1990.
Arthur Gertrude
Claas Selbstfahrende Erntemaschinen GmbH
Cuchlinski Jr. William A.
Husch & Eppenberger LLC
Kercher Kevin M.
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