Adaptability Analysis of Four-Leg Hydraulic Support with Large Mining Height under Impact Dynamic Load: In order to comprehensively analyze the adaptability of four-leg hydraulic support with large mining height under impact dynamic load, this paper adopts HyperMesh software to conduct a flexible pretreatment for the top beam, shield beam, and front and rear rods. The numerical simulation model of four-leg hydraulic support is established with ADAMS, where variable stiffness spring is used to replace columns to simulate their elastic characteristics. Building on analysis of the specific parameters of the support, this paper attempts to study the adaptability of the hydraulic support with impact dynamic load acting on the top beam. Additionally, the impact coefficient I and the excitation coefficient E have been introduced to transform the analysis results and optimize the evaluation method of the response degree of the top beam under impact load. Consequently, the response characteristics of the columns and pins at the hinged joints under various coupling states of the surrounding rock are found. Meanwhile, the paper has made a detailed comparison on the stress state of hydraulic support with different impact loads acting on the top beam. The adaptability change rule of the impact dynamic load under various impact conditions has been explored, which is of great significance to the optimization and strength design of four-leg hydraulic support with large mining height.

The structure and dynamics of a reciprocating pump liquid end affect the volumetric efficiency and net positive suction head. To match the kinematics with theoretical parameters, reciprocating pump valve motion and flow visualization tests and computational fluid dynamics (CFD) analyses were performed on a wing-guided bevel discharge valve in a horizontal quintuple single-acting reciprocating pump. The valve motion test results showed that the maximum pump valve displacement and the pump valve opening and closing durations were approximately 8.3 mm, 29 ms, and 38 ms, respectively. The corresponding flow visualization test results were 11.4 mm, 9.5 ms, and 35.5 ms. The valve closing durations obtained from the valve motion and flow visualization tests are approximately twice as high as the U-Adolph prediction. The maximum displacement obtained from the valve motion test is consistent with the U-Adolph prediction. Three-dimensional CFD analyses were performed to investigate the flow states, pressure, and velocity characteristics of the discharge valve opening. Finally, the proposed method was applied to develop a new horizontal quintuple single-acting reciprocating pump with a rated flow rate of 1250 L/min and pressure of 40 MPa. This developed pump exhibited good performance and excellent reliability.

In underground coal mining, there is an urgent need for reducing the noise, hydraulic shock, and leakage of reciprocating pumps. Flow pulsation is strongly dependent on the number of pistons for single-acting reciprocating pumps. Water hydraulic radial piston pumps (RPPs) allow the pistons to be radially distributed, which makes the multi-piston design easier compared to traditional single-acting reciprocating piston pumps. In this study, a novel valve-distributed water RPP was developed in which the star wheel assembled on the camshaft is used to drive the connecting rod-crosshead-piston assembly and realize the reciprocation of the piston and suction-discharge of the pump check valves. Mathematical and numerical models representing the RPP were built using MATLAB and AMESim, respectively. Three-dimensional computational fluid dynamics analyses were performed to investigate the internal flow field of the suction and discharge circuits as well as the flow characteristics of the discharge valve opening. A prototype pump was manufactured and tested for 200 h and then disassembled to evaluate the wear condition. The tested instant flow rates were lower than those obtained from the theoretical and simulation analyses. The experimental results also revealed that the average flow rates were almost independent of the output pressure and that the volumetric efficiency decreased with increasing rotational speed when the output pressure was larger than 25 MPa. Inspecting the disassembled water RPP revealed that scuffing problems occurred in the pistons, whereas the other components remained intact. The findings of this study offer a reference for water hydraulic power units in various applications and for investigating the intensity of water hydraulics.

Reciprocating piston pump is an important power equipment in coal mine production, so the research on condition monitoring and fault diagnosis of reciprocating piston pump is of great significance. It is challenging to extract fault information from monitoring data due to the complex underground environment and serious noise. The existing methods have the problems of insensitive feature extraction and low diagnostic accuracy. Based on this, a new fault diagnosis method for reciprocating piston pumps based on feature fusion of convolutional neural network (CNN) and transformer encoder is proposed. In this method, a multi-scale CNN encoder and transformer encoder are used to extract local and global features of signals in parallel, and a multi-scale convolution module is used to improve the diversity of local features. At the same time, before using the transformer encoder to extract global features, patch segmentation of monitoring signals is carried out in combination with the phase of the reciprocating piston pump crankshaft to reduce the influence of data randomness on global features and improve the interpretability of global features. In addition, a feature fusion module is constructed to realize the interaction and fusion of local and global features and improve the comprehensive characterization ability of the device state. The proposed method is applied to the fault diagnosis task of reciprocating piston pump. The experimental results show that the proposed method achieves a diagnostic accuracy of 99.145% ± 0.1576%, demonstrating its excellent performance. This accuracy rate is significantly higher than that of other existing methods, indicating that the proposed method can more accurately diagnose the faults of reciprocating piston pumps.