Open AccessResearch of diagnostic of combine harvesters at levels of hierarchical structure of systems and units of hydraulic system
Author(s) -
I. L. Rogovskii,
B S Liubarets,
Sergey Voinash,
В. А. Соколова,
А А Лучинович,
Марат Калимуллин
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1679/4/042038
Subject(s) - margin (machine learning) , computer science , field (mathematics) , work (physics) , operator (biology) , object (grammar) , control engineering , engineering , mechanical engineering , artificial intelligence , machine learning , mathematics , biochemistry , chemistry , repressor , transcription factor , pure mathematics , gene
The problem of diagnosing by external signs of the technical conditions of hydraulic systems of combine harvesters was formulated by the authors in the following way: we need to build a decisive rule that allows us to determine the presence of individual failures in the object from the observed external sign. As an example, we made a research on four general processes, often performed by the main hydraulic system of the harvester: raising the harvesting part, lowering the harvesting part, turning the unloading auger from the transport position to the working one, and turning the unloading auger from the working to the transport one. Failures characterize the technical condition of the harvester subsystems, which in turn consist of many different aggregates and parts. To restore working capacity, it is necessary to demonstrated failures of elements at the level of the hierarchical structure of the harvest, where this restoration is most effective for the conditions of a particular enterprise. Thus, for developing universal diagnostic tools, it is more efficient to identify failures at the lower level with a certain margin of diagnosis depth. The results of research made it possible to implement an intelligent operator support system for the collection and analysis of information about the technical condition of hydraulic systems of combine harvesters and imitate the work of a highly qualified specialist in the field of technical diagnostics.