Foreign objects intrusion into transmission lines can lead to serious troubles, using deep learning technology for foreign object detection has good performance and can reduce losses. Due to the complexity and diversity of the surrounding environment of the transmission lines, and the limitations of data acquisition methods, the image data of foreign objects invading transmission lines used in current research are extremely rare, and the types of foreign objects and background features are single. Deep learning requires large image data as a research driving force, Rare number of images leads to insufficient model fitting and affects the detection accuracy. We propose a Diverse Generation model, which can generate many images of foreign objects invading the transmission lines to provide support for deep learning, thereby solving the shortcomings of existing models. The results show that the dataset generated by our model has high quality and diversity, and can cover different scenes including those that are not convenient for data acquisition in reality. Thus, the accuracy of foreign objects detection can be effectively improved. This achievement provides a preliminary guarantee for abnormal detection of transmission lines, and helps to promote the integration of artificial intelligence technology in the power system.

With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors.

For the laser tracking and positioning system of a moving target using a four-quadrant detector, the accuracy of laser spot position detection has a serious impact on the tracking performance of the system. For moving target tracking, the traditional spot position detection method of a four-quadrant detector cannot give better consideration to both detection accuracy and operation speed. In view of this, an improved method based on piecewise low-order polynomial least squares fitting and a Kalman filter is proposed. Firstly, the tracking and positioning mathematical model of the system is created, and the experimental device is established. Then, the shortcomings of traditional methods are analyzed, and the improved method and the real-time tracking and positioning algorithm of the system are studied. Finally, through the experiment, the system operation effects are compared and analyzed before and after the improvement. The experimental results of system dynamic tracking show that, the least squares fitting of the experimental data using a 5-segment and quadratic polynomial can achieve better results. By using the improved method, the maximum tracking distance of a moving object is increased from 12 m to more than 30 m. At a distance of 7.5 m, the maximum tracking speed can reach 2.11 m/s, and the root mean square error (RMSE) of the position is less than 4.59 mm. At 15.5 m, the maximum tracking speed is 2.04 m/s and the RMSE is less than 5.42 mm. Additionally, at 23.5 m, it is 1.13 m/s and 5.71 mm.

This paper introduces a fault diagnosis method for mine scraper conveyor gearbox gears using motor current signature analysis (MCSA). This approach solves problems related to gear fault characteristics that are affected by coal flow load and power frequency, which are difficult to extract efficiently. A fault diagnosis method is proposed based on variational mode decomposition (VMD)–Hilbert spectrum and ShuffleNet-V2. Firstly, the gear current signal is decomposed into a series of intrinsic mode functions (IMF) by using VMD, and the sensitive parameters of VMD are optimized by using a genetic algorithm (GA). The Sensitive IMF algorithm judges the modal function sensitive to fault information after VMD processing. By analyzing the local Hilbert instantaneous energy spectrum for fault-sensitive IMF, an accurate expression of signal energy changing with time is obtained to generate the local Hilbert immediate energy spectrum dataset of different fault gears. Finally, ShuffleNet-V2 is used to identify the gear fault state. The experimental results show that the accuracy of the ShuffleNet-V2 neural network is 91.66% after 778 s.

Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF.

Vibration signal is often used in traditional gear fault diagnosis techniques. However, the working face of the scraper conveyor is narrow, harsh and easily explosive, so it is inconvenient to obtain vibration signals by installing sensors. Motor current signature analysis (MCSA) is a fault-diagnosis method without sensor installation, which is easier to realize in the mine. Therefore, a fault diagnosis method for local gear fault, which is based on bispectral analysis (BA) of analytical signal envelope obtained by processing a stator current under time-varying load condition, is proposed in our paper. In this method, the fault frequency component is enhanced by eliminating the interference of fundamental frequency and coal flow impact. Then, the enhanced fault frequency component is extracted by BA, and a quantitative analysis of the fault strength under time-varying load is carried out from the perspective of energy. Finally, the proposed method is verified on the number HB-kpl-75 scraper conveyor reducer, and the results show that this method can successfully diagnose the failure of the scraper conveyor gear under time-varying load conditions.