Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Intelligentization of wheel loader shoveling system based on multi-source data acquisition
Abstract To effectively reduce the operation intensity of drivers, it is essential to research the intelligent operation for various stages of loaders. The traditional operation mode of the loader requires the driver to manually complete the high repeatability and frequency operation of various stages, which makes the energy-saving characteristics of the loader particularly affected by the driver. Hence, this paper proposes intelligent operation methods for various stages of loaders based on the extraction and analysis of multi-sourced data characteristics of the experiment. Specifically, the automatic adjustment of the working device attitude in the shoveling stage is achieved. During the loading and unloading stages, the working device can be automatically lifted to the memorized angle, and the bucket can also be automatically leveled. The intelligent operation method solves the problem that the loader requires the driver to work with high intensity for various stages and improves the economic aspects.
Highlights The analysis based on the multi-sourced experimental data are carried out. Intelligent operation methods for various stages of loaders are proposed. Automatic attitude adjustment of the working device in shoveling stage is realized. In loading stage, the working device can be automatically lifted to the memorized angle. The bucket can also be automatically leveled in unloading stage.
Intelligentization of wheel loader shoveling system based on multi-source data acquisition
Abstract To effectively reduce the operation intensity of drivers, it is essential to research the intelligent operation for various stages of loaders. The traditional operation mode of the loader requires the driver to manually complete the high repeatability and frequency operation of various stages, which makes the energy-saving characteristics of the loader particularly affected by the driver. Hence, this paper proposes intelligent operation methods for various stages of loaders based on the extraction and analysis of multi-sourced data characteristics of the experiment. Specifically, the automatic adjustment of the working device attitude in the shoveling stage is achieved. During the loading and unloading stages, the working device can be automatically lifted to the memorized angle, and the bucket can also be automatically leveled. The intelligent operation method solves the problem that the loader requires the driver to work with high intensity for various stages and improves the economic aspects.
Highlights The analysis based on the multi-sourced experimental data are carried out. Intelligent operation methods for various stages of loaders are proposed. Automatic attitude adjustment of the working device in shoveling stage is realized. In loading stage, the working device can be automatically lifted to the memorized angle. The bucket can also be automatically leveled in unloading stage.
Intelligentization of wheel loader shoveling system based on multi-source data acquisition
Cao, Bingwei (Autor:in) / Liu, Xinhui (Autor:in) / Chen, Wei (Autor:in) / Li, Haomin (Autor:in) / Wang, Xianqing (Autor:in)
25.12.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| Europäisches Patentamt | 2024
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