Affected by respiratory infectious diseases, wearing masks has become the norm in many public places. Using machine inspection instead of manual inspection can greatly save human resources. Commonly used general target detection models based on deep neural networks often have a huge number of parameters, which is not conducive to terminal deployment and cost control. In response to these problems, this paper proposes a lightweight mask detection model G-YOLOv4 based on the improved general target detection model YOLOv4. Using GhostNet to decrease the parameters and computation of the discovery model, the network also increase the accuracy of the model discovery. The use of depth separable convolution instead of the general convolution in the original YOLOv4 further reduces the computational complexity of the model. Use the Mish activation function as the activation function of the GhostNet shallow network to optimize the convergence effect of the model. The experimental results show that the mAP of the optimized G-YOLOv4 algorithm reaches 93.21 %, which has an increase rate of 5.52 % compared with the original algorithm. The model parameter size is 11488785, which has a reduction rate of 82.05 % compared with the original algorithm. The calculation amount of the model is 7.05 GFLOPs, which has a reduction rate of 72.8 % compared with the original algorithm. The detection efficiency (FPS) of the model reaches 38.23, which has an increase rate of 204.14 % compared with the original algorithm.

A control strategy of repetitive control without inductorance decoupling was proposed to address the problem of high total harmonic distortion(THD) rate of the network-side current caused by the reduced stability of the rectifier module of the DC charging pile under weak grid as well as the dead zone and nonlinearity of switching devices during charging. Firstly, the parallel repetitive control was constructed in the inner current loop, and the proportional-integral(PI) + repetitive controller based on parallel structure was designed. For system compensation, a second-order low-pass filter was selected to correct the system, and the network-side current harmonics were actively suppressed without increasing the filtering device, which effectively improves the quality of grid-connected current. Secondly, based on the synthetic vector method, the controller parameters were designed to realize the elimination of main pole by establishing two synchronous rotation coordinate system vector differential equations, so as to realize the inductanceless decoupling to cope with the influence of network-side inductance fluctuation on the stability of the control system under weak grid. By theoretical analysis and simulation, the proposed control strategy was embedded into the self-developed digital signal processor for the rectifier module of DC charging pile, simulated dynamic and steady-state operation experiments were conducted, and comparative analysis was performed to prove the feasibility of the proposed control strategy.

In order to improve the accuracy of rolling bearing fault diagnosis when the motor is running under non-stationary conditions, an AC motor rolling bearing fault diagnosis method was proposed based on heterogeneous data fusion of current and infrared images. Firstly, VMD was used to decompose the motor current signal and extract the low-frequency component of the bearing fault signal. On this basis, the current signal was transformed into a two-dimensional graph suitable for convolutional neural network, and the data set was classified by convolutional neural network and softmax classifier. Secondly, the infrared image was segmented and the fault features were extracted, so as to calculate the similarity with the infrared image of the fault bearing in the library, and further the sigmod classifier was used to classify the data. Finally, a decision-level fusion method was introduced to fuse the current signal with the infrared image signal diagnosis result according to the weight, and the motor bearing fault diagnosis result was obtained. Through experimental verification, the proposed fault diagnosis method could be used for the fault diagnosis of motor bearing outer ring under the condition of load variation, and the accuracy of fault diagnosis can reach 98.85%.