Intelligent operation and maintenance is an important part of Industry 4.0. In order to realize the intelligent of plant equipment, it will make full use of artificial intelligence methods to evaluate the operating status of the equipment. Fault diagnosis of industrial equipment represented by bearings is critical in smart manufacturing. Early, online and accurate diagnostics can save the plant a lot of time and expense. With the development of sensor technology and deep learning technology, multi-sensor information fusion and convolutional neural network (CNN) provide a solution to the above problems. In this paper, based on the characteristics of mechanical vibration signal propagation in space, a new multi-sensor information fusion method is proposed to implement fault classification. This method constructs the time-domain vibration signals of multiple sensors from different position into a rectangular two-dimensional matrix, and then uses an improved 2D CNN to realize signal classification. The method is validated on the open dataset Case Western Reserve University, the University of Cincinnati IMS bearing database and the dataset form designed bearing fault test rig, has achieved prediction of 99.92%, 99.68%, and 99.25% respectively. Compared with the traditional 1D, 2D CNN and other fault classification methods, the model can utilize less data and computational complexity, achieve higher fault prediction accuracy.

Fault diagnosis methods based on deep learning and big data have achieved good results on rotating machinery. However, the conventional deep learning method of bearing fault diagnosis is mostly based on laboratory artificial simulation data, and there is an error with actual fault data, which will reduce the generalization performance of the deep learning method. In addition, labeled data are very precious in real industrial environment. Due to expensive equipment and personnel safety issues, it is difficult to obtain a large amount of high-quality fault labeling data. Therefore, in this paper, we propose a metric-based meta-learning method named Reinforce Relation Network (RRN) for diagnosing bearing faults with few-shot samples. In the proposed method, a 1D convolution neural network is used to extract fault features, and a metric learner is used to predict the similarity between samples under different transfer conditions. Label smoothing and the Adabound algorithm are utilized to further improve the performance of network classification. The performance of the proposed method is verified on a dataset which contains artificial damage and natural damage data. The comparison studies with other methods demonstrate the superiority of the proposed method in the few-shot scenario.

With the development of autonomous driving technology, some special vehicles should also be developed into autonomous driving vehicles. The robotic vehicles are widely used in engineering operations, and the robotic manipulator is a common tool mounted on robotic vehicles. The autonomous driving of robotic vehicles requires not only automatic movement, but also the automation of the robotic manipulator drive system. Therefore, this paper proposes an adaptive trajectory tracking control of manipulator based on sliding mode control (SMC), which can more accurately describe the joint space movement of the manipulator system under multi task coupling and external interference. Considering the multi-tasking situation, the influence of obstacle avoidance and base tilt on the trajectory of manipulator is investigated by using extreme learning machine (ELM), and the framework of machine learning is established. The output based on ELM-SMC is used as the bottom-layer control. Then an effective trajectory compensation method is proposed as the top-level control to avoid the error accumulation in the periodic repeated operation of the manipulator. Finally, the application effect of trajectory error tracking and compensation of manipulator in complex tasks of dynamic environment is verified by simulation and experiments, which lays a foundation for the stability control of manipulator in practical complex engineering applications. Note to Practitioners —The motivation of this paper is to solve the problem of poor trajectory tracking control accuracy of the manipulator caused by the change of the direction of the manipulator base. It is suitable for the high-precision position control of the manipulator assembled on the mobile robotic vehicle. Nowadays, the autonomous driving technology is developing rapidly, and the automation of traffic will be widely popularized in the future. Therefore, we should not only pay attention to the autonomous driving function of passenger vehicles, but also give consideration to the autonomous driving technology of robotic vehicles applied in engineering. It not only needs to complete the driving function, but also needs to ensure the reliable operation of the manipulator. In this paper, we mathematically deduce the control model of the manipulator. Then, we use SMC as the trajectory tracking control method of the manipulator, and use ELM to estimate the trajectory error caused by the end trajectory of the manipulator in the non-horizontal state of the base. Furthermore, we propose an optimization algorithm to optimize the compensation coefficient of cubic spline and give a reasonable compensation scheme. Through simulation and experiments, it is preliminarily verified that the schemes proposed in this paper are feasible, but they lack the application verification in the actual robotic vehicles, and the model fitting part of machine learning is completed with the help of the host computer, and the calculation ability of the controller of the actual manipulator may also be the limitation of completing this scheme.

Permanent magnet eddy current couplers (PMECCs) have the characteristics of contactless torque transmission, removal of torque ripple, smooth dynamic process, and adjustable speed, and can be used as couplings, dampers, brakes, and speed governors. Their applications in industry, vehicles, and energy fields are gradually expanding. At the same time, the requirements for the torque density and dynamic performance of PMECCs are increasing. Therefore, a large amount of research work has focused on the fast and accurate modeling, design, and optimization of PMECCs. This paper provides a survey on the development of PMECCs technology. The main topics include the structure and classification of PMECCs, modeling methods, loss and heat transfer analysis modeling, and optimization design. In addition, this paper shows the future trends of PMECCs research. All the highlighted insights and suggestions of this review will hopefully lead to increasing efforts toward the model’s construction and the optimal design of PMECCs for future applications.

In order to meet safety and stability grid-connected requirementsof high-power offshore wind turbines, the 10 MW wind power generation system composed of medium voltage six phase permanent magnet synchronous generator (MVSPMSG) and distributed neutral point clamped (NPC) converter is adopted. On the basis, a maximum power point tracking control (MPPT) method of 10 MW MVSPMSG based on neutral point potential balance is studied in the paper.Firstly, the optimal reference torque of MVSPMSG is calculated according to speed feedback, and the double d-q current control loop of stator is designed based on the doubly fed decoupling. The MPPT of MVSPMSG is realized by the closed-loop combination of torque loop and double d-q current loop. Secondly, in order to suppress the fluctuation of DC neutral point potential, a midpoint potential balance coordinated control method based on modulation coefficients and vector group selection is proposed. The mid-point potential fluctuations is suppressed by adjusting the magnitude of the modulation coefficient in the normal range. The vector group selection method is collaboratively added to the midpoint potential balance control based on small vectors when the modulation coefficient exceeds the limit. Finally, the effectiveness of the proposed MPPT control strategy and midpoint potential balance strategy are verified, which provides a theoretical reference for the control of high-power permanent magnet wind turbine.

Accurate wind power prediction can increase the utilization rate of wind power generation and maintain the stability of the power system. At present, a large number of wind power prediction studies are based on the mean square error (MSE) loss function, which generates many errors when predicting original data with random fluctuation and non-stationarity. Therefore, a hybrid model for wind power prediction named IVMD-FE-Ad-Informer, which is based on Informer with an adaptive loss function and combines improved variational mode decomposition (IVMD) and fuzzy entropy (FE), is proposed. Firstly, the original data are decomposed into K subsequences by IVMD, which possess distinct frequency domain characteristics. Secondly, the sub-series are reconstructed into new elements using FE. Then, the adaptive and robust Ad-Informer model predicts new elements and the predicted values of each element are superimposed to obtain the final results of wind power. Finally, the model is analyzed and evaluated on two real datasets collected from wind farms in China and Spain. The results demonstrate that the proposed model is superior to other models in the performance and accuracy on different datasets, and this model can effectively meet the demand for actual wind power prediction.