Search

The hydraulic support equipment is usually used to support the roof in underground mining. During this process, the support bears frequent impact load, which greatly weakens its dynamic stability. To study the dynamic response characteristics of the support, when it bears the impact load at the top beam and shield beam, the numerical model of the support is established using ADAMS and the equivalent variable stiffness damping system was used to replace the column system. An active static load is applied vertically above the top beam to simulate the roof gravity force, and the impact load is applied downward perpendicular to the top beam and shield beam. Based on the above model, the dynamic response of the key parts of the support, when different impact loads are applied to the top beam and shield beam, and the influence of offset loading on the stability of the support are analyzed. Then, when considering asymmetric bracing conditions, the dynamic characteristics of hydraulic support under impact load are discussed by setting different initial support forces. The results show that when under a single impact load, the load variation coefficient of the front column reaches 1.41, which is the most sensitive to the impact load at the front end of the top beam. When the top beam and shield beam bear the impact load at the same time, the support hinge joint response more violently to the impact load acting on the top beam end and the upper area of the shield beam, and the maximum load change coefficient is 0.64, which greatly affects the overall stability of the hydraulic support. When the support bears to lateral torque, the weakening effect of offset load acting on different positions on the bearing performance of the support is the same, and the magnitude of offset load is positively correlated with the weakening effect. When considering the different initial load ratios (ILR) of the column, it is found that the hinge joint of top beam and shield beam is the most sensitive to the change of ILR, and the insufficient initial support of the front column has the greatest weakening effect on the bearing performance of the support. The research results can help to optimize the structure of the support shield support and improve the bearing reliability of the hydraulic support.

The problems of insufficient recognition accuracy, poor real‐time performance and lack of consideration of actual working conditions in the process of intelligent construction of coal mines make this technology still in the research stage and not applied in practical engineering. The purpose of this paper is to establish an accurate and real‐time recognition model, which can quickly distinguish the vibration acceleration signals of coal and gangue under the influence of external factors such as impact position, velocity, and direction by using the different physical properties of coal and gangue particles. Therefore, the accuracy and real‐time of coal gangue recognition model established by different convolutional neural networks (CNN) structures and different position signal input are studied. First, to meet the real‐time requirements, an original CNN recognition model composed of single convolution layer and single pooling layer is established, and the data collected by seven sensors are input in the form of two‐dimensional matrix. However, the stability of the training and test results is insufficient. To solve this problem, once improved CNN (OI‐CNN) recognition model with multiconvolution layers and multipooling layers is built by deepening the network. The experimental results show that the stability and accuracy are improved, but the real‐time performance is poor. Furthermore, through parameter adjustment, the OI‐CNN is changed to the twice improved CNN (TI‐CNN), and the sensor data at different positions are input in the form of one‐dimensional vectors. The results show that the accuracy and real‐time performance of the TI‐CNN coal gangue recognition model are further improved. Finally, according to the research purpose of this paper, the weights of CNN indexes are given, and a multi‐index comprehensive evaluation system (MICES) is established. With the original CNN recognition model as the control, the OI‐CNN recognition model and the TI‐CNN recognition model at different positions are quantitatively compared to obtain the comprehensive evaluation scores of each model. The results show that the MICES of the coal gangue recognition model established based on the TI‐CNN structure and the data input of a single position sensor is the highest, while the sensor position has little effect on the recognition results. [ABSTRACT FROM AUTHOR]

In order to obtain the natural frequency and vibration mode of the chain transmission system of the scraper conveyor,the meshing process of the sprocket and the round link chain is analyzed to obtain the key meshing state during the meshing process. The modal analysis of the different meshing states is carried out by ANSYS software. A general natural frequency change law in a complete meshing process is obtained,by the method of fitting data points and the natural frequency range of the chain transmission system is obtained.Which effectively solve the problem that single sprocket static mode analysis cannot meet practical condition.The solving results provide the basis for the dynamics research and optimization design of the chain transmission system of the scraper conveyor.

In order to ensure intelligent welding process is suitable for production of structural member of support, improve equipment utilization rate, guarantee welding quality, and give full play to advantages of intelligent welding workshop, production information management system of intelligent welding workshop for hydraulic support based on MES was studied. The overall system model and its network architecture were established, functional requirements of the system were analyzed, and the system database model was established. The design of data acquisition module of intelligent welding workshop production information was introduced. The module realizes comprehensive collection of scene information of workshop. The practical application results show that the system realizes effective data communication between enterprise decision-making level and production site, and ensures production quality and efficiency of hydraulic support.

The bi-tandem axial piston pump is an indispensable powerhouse in high-pressure and high-power engineering hydraulic systems, with its output flow response characteristics under pressure disturbance exerting a significant influence on the working process of double pumps. Unfortunately, the stability of the original single-loop mechanical–hydraulic servo control system is sensitive to unpredictable interference. To alleviate this quandary, this paper proposes a dual-loop positive flow control method for the flow control of the bi-tandem axial piston pump, establishes a mathematical model of the bi-tandem axial piston pump with dual-loop positive flow control, and establishes a simulation model based on Simulink. The validity of the model is verified by experiments. The performance advantages of the dual-loop positive flow control method relative to the single-loop positive flow control method are analyzed. The results show a faster response speed and smaller steady-state error with the dual-loop method, which performs better than the original single-loop positive flow control. Furthermore, the study examines the influence of different forms, degrees, and directions of pressure disturbance on the dynamic response characteristics of the bi-tandem axial piston pump. Symmetric pressure disturbance results in an increase in the maximum relative error of the output flow proportional to its degree. Notably, the influence of asymmetric pressure disturbance on the output flow of the double pumps possesses characteristics of a superimposable nature, and the steady-state value of the output flow is highly dependent on superimposed pressure disturbance and less affected by the action time point of asymmetric pressure disturbance. Further, the unloading pressure disturbance exerts less influence on the system compared to the loading pressure disturbance. This paper provides valuable insights into improving the response speed and control accuracy of bi-tandem axial piston pumps equipped with positive flow control. [ABSTRACT FROM AUTHOR]