The effective fault diagnosis of bearing can guarantee the safety of rotating machinery and is very important for its stable operation. The information fusion of multi-sensor data has been a feasible method to enhance the performance of fault diagnosis. However, how to fuse the joint information from different channels or even different kinds of sensors is still an important challenge. The present study proposes a novel multi-sensor information coupling network (MICN) for bearing fault diagnosis, which handles the signals from the same or different types of sensors, and the deeper features can be extracted from multi-sensors independently and simultaneously fused layer by layer. Especially, during the multi-layer feature fusion process, a novel feature-level information coupling model is developed based on the mutual attention mechanism. Finally, to validate the efficiency of the proposed method, several different experiments are designed, and the results show validity and superiority.

Microrobots can operate in tiny areas that traditional bulk robots cannot reach. The combination of acoustic actuation with microrobots extensively expands the application areas of microrobots due to their desirable miniaturization, flexibility, and biocompatibility features. Herein, an overview of the research and development of acoustics-actuated microrobots is provided. We first introduce the currently established manufacturing methods (3D printing and photolithography). Then, according to their different working principles, we divide acoustics-actuated microrobots into three categories including bubble propulsion, sharp-edge propulsion, and in-situ microrotor. Next, we summarize their established applications from targeted drug delivery to microfluidics operation to microsurgery. Finally, we illustrate current challenges and future perspectives to guide research in this field. This work not only gives a comprehensive overview of the latest technology of acoustics-actuated microrobots, but also provides an in-depth understanding of acoustic actuation for inspiring the next generation of advanced robotic devices.

To study the variation rules of nonlinear stiffness of double-row angular contact ball bearings (DR-ACBB), this paper proposed a general mathematic model for DR-ACBB under three different configurations based on the improved quasi-static model of ball bearings, an explicit expression stiffness matrix of DR-ACBB is analytically derived, and a double-layer nested iterative algorithm based on the Newton–Raphson method is designed to realize the efficient solution of the proposed model. Then, the effects of the preload, speeds, and loads on the nonlinear stiffness variations of DR-ACBB under different arrangements are comparatively analyzed. The results show that DR-ACBB under the DB and DF configurations have the same variation rule in axial and radial stiffness; that is, a nonlinear soft-spring stiffness characteristic (i.e., the stiffness decreases with the external load) within the low-speed range and light load condition, and a nonlinear hard spring stiffness characteristic (i.e., the stiffness increases with the external load) within the high-speed range or heavy load condition.

Addressing the synchronization issue between lower limb exoskeletons and the limbs of wearer, this paper proposes a deep learning hybrid model for identifying gait patterns and transition states using only lower limb angle data. The proposed hybrid model integrates one-dimensional convolutional networks with BiGRU. The model accurately predicts subsequent gait patterns by identifying transition states, thus achieving smoother transitions during movement and enhancing wearer comfort. Compared to other models, this paper introduces a novel evaluation index named HIAM, which demonstrates the comprehensive performance advantage of the proposed model. The model classifies five gait patterns and eight transition states using only the angle data from the thighs and shanks of wearer. The classification accuracy and F1-score are 99.05% on the validation set, achieving 99.11% accuracy and F1-score on the test set. The HIAM reaches 99.29 on the validation set and 99.36 on the test set.