Driven by Industry 4.0 and Made in China 2025, mobile robots have ushered in rapid development. In this paper, a mechanical structure design scheme of heavy-duty mobile robot is proposed, including the design of drive scheme, etc. Based on the designed mechanical structure of heavy-duty mobile robot, a finite element model of the robot mechanical structure is established, and the stress distribution and deformation under the full-load condition are obtained through the static characterization of the robot model, and finally the road experiment is carried out for the robot, and the experimental results show that the mobile robot operates well under various working conditions and reaches the expected goal of the design. The experimental results show that the mobile robot runs well under various working conditions, and the heavy-duty mobile robot reaches the expected goal of the design.
Fault diagnosis is an important part of the intelligent development of industrial robots. Aiming at the problem of lack of data in industrial robot fault diagnosis, this paper introduces a fault diagnosis method based on digital twin and data-driven fusion. The consistency between the model and the actual device is achieved by constructing a digital twin model of the industrial robot and mapping it to the actual industrial robot in real time. In order to solve the problem of lack of data, the fault injection technique was used to inject fault data into the digital twin model and combined with historical data to construct a training dataset. Through simulation experiments on real welding robot data, the machine learning fault diagnosis model was trained and evaluated for precision, recall and F-Score. The experimental results show that this method can effectively solve the problem of lack of fault data and train a reliable fault detection model, providing an effective solution for industrial robot fault diagnosis.
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