Graph data-driven methods have gradually attracted attention in transfer learning-based machine fault diagnosis. However, there are still some limitations. First, feature space deviation exists in the mapping of relationship information in the source and target domains during the graph construction, bringing negative transfer and limiting constructed graph quality. Second, interpretability of relationship information during graph construction for machine fault diagnosis is lacking. In this article, a dynamic graph-driven rotating machine fault diagnosis method via adaptively updating cross-domain relationship information is proposed. A dynamic transfer graph (DTG) construction framework is developed to keep the relationship information mapping in the cross-domain consistent. Meanwhile, an improved classification loss, which consists of multiscale cross-entropy loss and multiscale domain adaptation loss, is designed to construct high-quality DTG. In addition, the working mechanism of relationship information in DTG is revealed by exploring the changes of intraclass edges, interclass edges, and cross-domain edge connections in the graphs. Experimental results demonstrate its effectiveness.

This article proposes a method to remotely control the robot in the laboratory through the Kinect camera to solve the impact of the Covid-19 epidemic on the laboratory teaching experience which allows users to remotely control robots through their own body movements to understand the principles of robots. It is used to solve the problem of fewer students willing to participate in robot remote education. In this study, the Azure Kinect DK camera was used to collect the motion posture of the upper limbs of the human body. The Kinect camera calculates the frames of human arm joints’ motion. The control system calculates the direction of motion of each joint of the human body based on the quaternion by mapping the heterogeneous human joints with the robot joints. Make the posture of the human arm swing correspond to the posture of the robot’s movement. Thus, the robot in the laboratory can be driven remotely through Azure Kinect DK. By using the method described in this article, students use the camera’s motion capture system to remotely manipulate the robot to grab some simple objects. Through the method described in this research, students can carry out some simple operations on the robots in the laboratory from remote. So it is convenient for students to understand the basic principles of robots and achieve the purpose of better remote experimental teaching. At the same time, students can get practical application of motor servo control, ergonomics, physical simulation engine, digital twin system, etc.

With the popularity of virtual reality technology, it’s application in the field of engineering educational and training has gradually increased. This article introduces a multiplayer mixed reality (MR) application that supports remote participants to operate engineering equipment and learn operation process in the shared virtual environment, and it explores the impact of network factors on user experience. According to the characteristics of the application, it proposes a corresponding adaptive strategy based on an existing synchronization algorithm, meantime, a comprehensive evaluation method is proposed.

A novel adaptive sliding mode control algorithm is derived to deal with synchronized joint position tracking control of robot manipulators. The proposed algorithm does not require the precise dynamic model, and is very practical. The cross-coupled technology is incorporated into the adaptive sliding mode control architecture through feedback of joint position errors and synchronization errors. Its robustness is verified by the Lyapunov stability theory. Simulation results obtained from a 3-link non-linear planer robot manipulator demonstrate the effectiveness of the approach under various disturbances.

A novel adaptive sliding mode control (ASMC) algorithm is derived to deal with seam tracking control problem of welding robotic manipulator, during the process of large-scale structure component welding. The controllers robustness is verified by the Lyapunov stability theory, and the analytical results show that the proposed algorithm enables better high-precision tracking performance with chattering-free than classic sliding mode control (SMC) algorithm.