When mechanical products work in complex environments, it is imperative to build an optimal maintenance strategy, based on accurate positioning of fault locations and prediction of fault conditions. Based on digital twinning technology, this paper proposes a “super-network-warning features” fault prediction and maintenance method. According to the digital twin five-dimensional structure, a three-layer super-network model is constructed, providing a quantitative research for data among heterogeneous subjects in digital twinning. Early-warning-featurefeature feature featurefeatures in the physical layer, virtual layer and service layer are selected as input parameters of the fault prediction model to accurately predict the cause of the fault. Then, using the simulation and optimization functions of the virtual model in digital twinning, a real-time maintenance strategy is formulated for the causes of the fault. It supplements the missing link between fault prediction and maintenance. Taking an aero-engine bearing as an example, this method is compared with a traditional method. The results show that the model prediction error of this method is better than the traditional method.

Facing some special operating environments or conditions, existing control methods for the peg-in-hole assembly guided by robots always have their own disadvantages, for example, low efficiency or poor adaptability. For the above problem, in this article, a new circular peg-in-hole assembly control strategy is proposed for the 6-Degree of Freedom (DOF) robot based on hybrid visual measurements, avoiding peg-in-hole contacts during the robotic operation. In the strategy, the pose of the monocular camera mounted at the end-effector is adaptively adjusted to improve the image quality through an algorithm based on the rough pose measurement of the target hole by the binocular camera; the accurate 3-D pose of the hole is determined by an algorithm based on processing of high-quality images and the compensation of the orientation error. Combined with the robotic collision-free path planning, the automatic peg-in-hole assembly can be implemented in real setting. The assembly precision of the robotic system based on the proposed method is validated and discussed based on experimental results. Then, the minimal peg-in-hole interval relative to the alignment error is modeled through the spatial relation analysis to analyze the applicable condition of the robotic system with the control strategy. Also, the reliability of the proposed strategy is verified through experimental tests under some applicable conditions. Finally, suggestions and plans of future works are discussed for further extension of the application area of the proposed strategy, such as fields of precision and ultraprecision manufacturing. This contribution has the major significance on the automatic peg-in-hole assembly under 3-D operating environment.

Disassembly plays an important role in the production process. Screw automatic unfastening guided by a robot has been widely used in the fields of industrial manufacturing and maintenance. Different from the previous studies that have used a flexible effector and expensive sensors, this paper presents a novel unfastening strategy based on robot vision for a hexagonal screw with an arbitrary loose state. In a robotic system, an industrial camera and a servo unfastening tool are installed at a robotic end-effector. The main contributions of this work are as follows. A camera pose adjustment method is proposed to obtain high-quality images of a target screw. The hexagonal screw pose calculation method based on the geometric analysis is developed to complete the screw–tool engagement. The cooperated motion of a robot and an unfastening tool is planned for the screw unfastening action. The effectiveness of the proposed control strategy is verified by experiments, and the influence of the motion speed on the unfastening quality is analyzed using the torque data collected by the unfastening tool. The analysis results can provide a significant foundation for the motion parameter selection in the proposed strategy.