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.

Ionic liquids (ILs) are molten organic salts consisting of organic cations and weakly coordinating organic/inorganic anions at room temperature. ILs have excellent physical and chemical properties such as high thermal stability, high combustible temperature, high miscibility with organic compounds and so on, making them good candidates for high performance lubricants and lubricant additives. The functional designability of ILs makes them novel lubrication materials that can break through the bottleneck of the active control of friction and lubrication. This paper firstly briefly introduces how to design the physical and chemical properties of the ILs required for different friction conditions by bonding specific cations with anions. Then, the lubrication mechanisms of ILs as base lubricants and additives for oils and water are focused on. The correlation between the structure of ILs and the lubrication results are established, which can guide the structural design of ILs in different applications. The response behaviors of friction characteristics under external electric fields are analyzed, which can provide a theoretical basis for the intelligent control of friction based on ILs.

In most existing bolt assembling operations, the human-machine collaboration (HMC) is used to guarantee both the operational efficiency and the connection quality. However, the huge labour intensity and costs are still needed when there exist bolts with huge quantity and multiple models in some special working environments, for example locomotive maintenance and automobile assembly. Actually, the robotic application can better solve the above problem. The main reason why this application is limited in the bolt assembly is due to the technological barriers related to the intelligent control. This paper firstly introduces the HMC-based and the robot-guided bolt assembly processes in detail, then summarizes the latest researches related to the significant technologies needed in the robot-guided assembly to show their present status and future trends. Finally based on the technology limitations this paper further presents some suggestions for future studies of each technology, which attempts to help readers broaden their research thoughts and speed up the development of the robot-guided bolt assembly.