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.

It is well known that complex mechanical equipment has many differnt the failure modes, and monitoring data for fault conditions are scarce. Therefore, research on fault detection for reciprocating plunger pumps with fault-free data is significant for theory and application. Due to the lack of prior knowledge of faults, it is always a great challenge for researchers to extract fault features from signals. In this paper, an innovative fault detection method for a reciprocating plunger pump is proposed based on an unsupervised feature encoder (UFE) and minimum covariance determination (MCD). Firstly, a criterion based on mutual information maximization for local and global features is proposed for unsupervised feature extraction. In addition, an unsupervised training strategy based on negative sampling is proposed to train the encoder, so that the model can converge quickly and improve the stability of model training. A fault detection algorithm for a reciprocating plunger pump is proposed based on a UFE and MCD. Finally, the effectiveness and superiority of the proposed method are verified with the measured data for a reciprocating plunger pump. The results show that the proposed method can accurately detect the faults in the reciprocating plunger pump with a detection accuracy of more than 98%. Compared with other methods, the proposed fault detection algorithm has better applicability and accuracy for fault detection with fault-free data.

Accurately measuring the mechanical properties of high-strength stainless steels is of great significance for ensuring structural safety, predicting long-term performance and optimizing design. However, the standardized tensile test specimens used to obtain strength properties must be fabricated from bulk materials from an in-service structure or component, result in the loss of structural integrity or a reduction in remaining service life. Small punch tests require only miniscule material samples and are widely used to estimate in-service component material characteristics. This study performed small punch tests on three high-strength stainless steels—X17CrNi15-2, 15-5PH, and PH13-8Mo—to obtain the correlations between the small punch tests and the uniaxial tensile test results for each material. The estimated yield stresses obtained from the small punch tests were distributed within a factor of 1.5 on both sides of the unity line when compared with those obtained from the uniaxial tensile tests; the ultimate strength values estimated from the small punch tests correlated quite well with those obtained from the uniaxial tensile tests. An iterative finite element analysis was subsequently established to simulate the small punch tests and thereby obtain the coefficients for a Ludwik power law correlation representing the true stress–strain properties. The finite element predicted force–deflection curves agreed well with the small punch test results, which were also compared with the predictions obtained from empirical equations proposed in previous studies. Finally, the fracture mechanisms of the small punch test specimens were characterized through scanning electron microscopy, indicating that the dimple fracture mechanism dominated the failure mode. The results of this study are expected to inform the application of small punch test to evaluate in-service high-strength stainless steel materials.

Laser powder bed fusion (LPBF) can fabricate hydraulic components with significant weight reduction, and in this study, small punch tests (SPTs) evaluated the tensile and fracture properties of four stainless steels (30Cr13, 316L, 15-5PH, 17-4PH), alongside metallographic, scanning electron microscope (SEM), and Electron Backscatter Diffraction (EBSD) analyses which examined their fracture modes, grain orientation, phase distribution, and grain boundary distribution. The tensile property results showed ductility rankings as 316L > 17-4PH > 15-5PH > 30Cr13, with correlations between R_p0.2 and R_m from SPT and uniaxial tensile tests for all four, while high-magnification SEM fractographs revealed ductile dimples on 15-5PH, 17-4PH, and 316L SPT specimens versus distinct cleavage fracture in 30Cr13. EBSD analysis indicated austenite content order as 316L > 17-4PH > 30Cr13 > 15-5PH, grain size order as 316L > 17-4PH > 15-5PH > 30Cr13, and high-angle grain boundaries ranking as 15-5PH > 30Cr13 > 17-4PH > 316L; additionally, notched SPT specimens inspected per EN 10371 for fracture toughness showed J-integral (J_IC) values in the order 316L > 17-4PH > 15-5PH > 30Cr13, consistent with ductility and grain size results.

The contact behavior of a stepseal affects its reliability and remaining life. In this study, experimental and numerical analyses were performed to investigate the contact behavior of a typical stepseal. Its dimensions and material properties were also tested. The Mooney–Rivlin hyperelastic model was used to fit the stress–strain data of the rubber. A static contact pressure of 105.1 MPa was obtained using the Fujifilm pressure measurement film. A finite element model of a stepseal with reasonable element sizes was established. A comparison between the experimental results and finite element (FE) predictions shows that the finite element method underpredicts the maximum static contact pressure and contact length. For the maximum contact pressure, the test results were approximately 30.4% higher than those of the FE predictions.

With the increase in power and flow of emulsion pumps, pump valves are more likely to be damaged during long working hours, Therefore, it is important to ensure the reliability of pump valves at all stages. However, there are still some problems that need to be solved urgently. For example, whether the actual dynamic characteristics of the valve are consistent with the simulation results, and whether the simulation can replace experiments to provide a reference for the design improvement of the valve. At the same time, what is the failure mechanism of the valve, and what methods can be used to find the early failure of the valve. Therefore, the dynamic impact contact characteristics and fault diagnosis of valves of reciprocating piston pumps were investigated both experimentally and numerically. They were analyzed using AMESim simulation and U-Adolph theory, respectively, to predict the dynamic characteristics of the valve. The effect of spring stiffness on the dynamic characteristics of the valve was also investigated. The numerical analysis and analytical results are in good agreement with the experimental results, which provide an important reference for the development of new horizontal five-cylinder single-acting reciprocating pumps. In addition, finite element analysis was used to study the impact contact characteristics of the discharge valve closing under different valve diameters. The failure mechanism of the pump and valve was revealed, a fault diagnosis method was proposed, and the effectiveness of the method is verified by experiment.