Background: In recent years, surgical robots have become an indispensable part of the medical field. Surgical robots are increasingly being used in the areas of gynaecological surgery, urological surgery, orthopaedic surgery, general surgery and so forth. In this paper, the development of surgical robots in different operations is reviewed and analysed. In the type of master-slave surgical robotic system, the robotic surgical instrument arms were located in the execution terminal of a surgical robot system, as one of the core components, and directly contact with the patient during the operation, which plays an important role in the efficiency and safety of the operation. In clinical, the arm function and design in different systems varies.Furtherly, the current research progress of robotic surgical instrument arms used in different operations is analysed and summarised. Finally, the challenge and trend are concluded.Methods: According to the classification of surgical types, the development of surgical robots for laparoscopic surgery, neurosurgery, orthopaedics and microsurgery are analysed and summarised. Then, focusing on the research of robotic surgical instrument arms, according to structure type, the research and application of straight-rod surgical instrument arm, joint surgical instrument arm and continuous surgical instrument arm are analysed respectively.Results: According to the discussion and summary of the characteristics of the existing surgical robots and instrument arms, it is concluded that they still have a lot of room for development in the future. Therefore, the development trends of the surgical robot and instrument arm are discussed and analysed in the five aspects of structural materials, modularisation, telemedicine, intelligence and human-machine collaboration. Conclusion:Surgical robots have shown the development trend of miniaturisation, intelligence, autonomy and dexterity. Thereby, in the field of science and technology, the research on the next generation of minimally invasive surgical robots will usher in a peak period of development.

Ultrasonic transducer based on rare-earth giant magnetostrictive materials was designed in accordance with the technical requirements of ultrasonic surface strengthening. The whole structure of the transducer was designed. Modal analysis is made to get the natural frequency of the compound oscillator. The working frequency of the transducer should be guaranteed at about 15.2 kHz and the composite oscillator should have relatively better vibration mode. The magnetic field of the transducer is well sealed and the transducer will not show obvious magnetic flux leakage phenomenon. Which shows the rationality of structural design. Based on this transducer, the ultrasonic surface strengthening experiment on 40 steel was conducted. The surface roughness and hardness of the parts were analyzed after the experiment. The results show that the surface of the parts reach the mirror surface result after the ultrasonic strengthening. When compared to the previous process, the roughness decreases by about 75%, and the surface hardness increases by more than 20%.

To meet the high-speed requirements on pick-and-place or obstacle avoidance actions of industrial robots, this article puts forward a novel high-speed and smooth transfer control algorithm for spatial elliptic trajectory. For the smooth motion at high speed, a modified S-shaped acceleration/deceleration (ACC/DEC) control algorithm based on a piecewise continuous jerk curve is proposed. With the mathematical model and interpolation point calculation algorithm of spatial elliptic trajectory, the interpolation point information can be obtained. Then, the speed look-ahead algorithm is used to obtain the optimal speed at the switching point for trajectory transfer. From the experiment and simulation of a sorting case, it can be analyzed that the velocity curve turns out to be smoother and the run time reduced by 33.69 %. This transfer algorithm can guarantee the smooth motion for industrial robots and also improve the motion efficiency significantly.

Accurate and efficient condition monitoring is the key to enhance the reliability and security of wind turbines. In recent years, an intelligent anomaly detection method based on deep learning networks has been receiving increasing attention. Since accurately labeled data are usually difficult to obtain in real industries, this paper proposes a novel Deep Small-World Neural Network (DSWNN) on the basis of unsupervised learning to detect the early failures of wind turbines. During network construction, a regular auto-encoder network with multiple restricted Boltzmann machines is first constructed and pre-trained by using unlabeled data of wind turbines. After that, the trained network is transformed into a DSWNN model by randomly add-edges method, where the network parameters are fine-tuned by using minimal amounts of labeled data. In order to guard against the changes and disturbances of wind speed and reduce false alarms, an adaptive threshold based on extreme value theory is presented as the criterion of anomaly judgment. The DSWNN model is excellent in depth mining data characteristics and accurate measurement error. Last, two failure cases of wind turbine anomaly detection are given to demonstrate its validity and accuracy of the proposed methodology contrasted with the deep belief network and deep neural network.