Narrow Results

This paper explores the performance and obtains a reasonable cleaning effect of the cleaning system of combine harvester and studies the relationship between the cleaning effect of the combine harvester cleaning system and its influencing factors. We established a neural network model between the cleaning loss rate and the clean system parameters. First, we tested the results of the cleaning performance of each group under different combinations of conditions, and analyzed the direct or indirect relationship between the cleaning loss rate and the parameters in the experiment under each working condition. Then, according to the experimental data obtained in the experiment, we predict the clearance loss rate for several sets of conditions by this model. The experimental results show that the prediction results of the model can meet the experimental requirements under the condition that the accuracy is not very high.

The intelligent control of cleaning of rice–wheat combined harvester is a complex problem, which includes the initial setting of cleaning control, judgment of cleaning loss state, cause analysis and selection of corresponding control strategies and many other sub-problems. The knowledge contained in these sub-problems, including knowledge representation methods and reasoning strategies, is different. Therefore, this paper decomposes the complex problem of cleaning control into a sub-problem of hierarchical structure, and constructs a knowledge model of cleaning control based on binary tree structure. In this way, the cleaning control problem can be decomposed into a small set of sub-problems by the judgment of the nodes of the binary tree, until the sub-problems are small enough to be solved directly so as to get the solution of the original problem. It is proved by examples that this method is of great significance to improve the efficiency of knowledge acquisition, management and maintenance of the expert system of rice–wheat combine harvester, and to enhance the knowledge service ability of the expert system of rice–wheat combine harvester. This method can also be used for reference in other fields.

The intelligent regulation and control strategies for rice and wheat combine harvesters’ operation are lacking and the rule of parameter matching is fuzzy in China, around these issues. The dynamic correlation control law among the parameters of rice and wheat, cleaning operation parameters of combine harvesters, the cleaning loss rate and impurity rate, and so on are studied. The intelligent control model for the rice and wheat combine harvester is established based on case-based reasoning (CBR). According to different rice and wheat varieties, water content and other rice and wheat properties, the control scheme of cleaning fan speed, air distributor plate angle and upper sieve opening with low cleaning impurity rate and cleaning loss rate is provided. Through the development of web-based cleaning intelligent control expert system and experimental evaluation, the feasibility and effectiveness of the CBR method in the intelligent control filed of rice and wheat combine harvesters are verified.

Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.