Internal leakage is the most common failure of hydraulic cylinder; when it increases, it decreases volumetric efficiency, pressure and speed of the hydraulic cylinder, and can seriously affect the normal operation of the hydraulic cylinder, so it is important to measure it, especially to measure it online. Firstly, the principle of internal leakage online measurement is proposed, including the online measurement system, the fixed mode of the strain gauge and the mathematical model of the flow-strain signal conversion. Secondly, an experimental system is established to collect internal leakages and strain values, and the data is processed. Finally, the convolutional neural network (CNN), BP neural network (BPNN), Radial Basis Function Network (RBF), and Support Vector Regression (SVR) are used to predict the hydraulic cylinder leakage; the comparison of experimental results show that the CNN has high accuracy and high efficiency. This study provides a new idea for online measurement of small flow on other hydraulic components.

In this study, the mechanical properties of a combined seal ring under different loads were numerically calculated using ANSYS. The effect of the working pressure and pre-compression ratio of a rubber O-ring on the contact stress of the combined seal ring was studied. The influence of the wear ring’s chamfer, thickness, and width on the contact stress and contact force of the combined seal ring was analyzed. Studies have shown that it is particularly important to select a compression ratio that is suitable for the working conditions. Under the same conditions of working pressure and compression ratio, upon increasing the wear ring chamfer, the contact pressure is decreased due to the decreasing contact bandwidth between the wear ring and the cylinder wall. This has little effect on the contact stress of the combined seal ring as well as the contact force, while the width of the wear ring is proportional to the latter.

Internal leakage diagnosis in a hydraulic cylinder is a key technique for the maintenance of hydraulic systems. However, it is difficult to diagnose the internal leakage under different low loads. To solve this problem, a novel fault diagnosis method based on the optimization deep belief network (DBN) combined with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is proposed to treat the collected AE signals. The raw AE signals are decomposed into a set of intrinsic mode functions (IMFs) by using CEEMDAN. Subsequently, according to the decreasing order of the Pearson correlation coefficient values, the first five IMFs are selected for signal reconstruction to suppress the abnormal interference from noise. The reconstructed signals are regarded as the input of the optimization DBN, and the particle swarm optimization simulated annealing (PSOSA) algorithm is adopted to identify the four internal leakage levels. The experimental results show that the proposed method exhibits a higher classification accuracy than other methods under different low loads. This result validates the effectiveness and superiority of the proposed approach to realize internal leakage diagnoses under different low loads.

The formation of hydrodynamic lubrication is analyzed based on an experimental study of the hydrodynamic lubrication characteristics between two parallel surfaces with different types of surface texturing. The study considers planar samples of C45 steel and accounts for the thermal expansion of 304 stainless steel in the experimental apparatus at oil temperatures of 323.15 K and 353.15 K. The experiments involve the use of a rotating rheometer at various angular speeds, with the effect of thermal expansion that occurs in actual applications being simulated by varying the gap between the planar sample surfaces. The experimental results show that (i) the lubrication properties of the planar textured samples depend strongly on temperature and (ii) the load-carrying capacity of the textured samples is considerably greater than that of the non-textured samples.

In order to ensure the blade strength of large-scale wind turbine, the blunt trailing edge airfoil structure is proposed, aiming at assessing the impact of the trailing edge shape on the flow characteristics and airfoil performance. In this paper, a Joukowsky airfoil is modified by adding the tail thickness parameter K to achieve the purpose of accurately modifying the thickness of the blunt tail edge of the airfoil. Using Ansys Fluent as a tool, a large eddy simulation (LES) model was used to analyze the vortex structure of the airfoil trailing edge. The attack angles were used as variables to analyze the aerodynamic performance of airfoils with different K-values. It was found that when α = 0°, α = 4°, and α = 8°, the lift coefficient and lift–drag ratio increased with increasing K-value. With the increase in the angle of attack from 8° to 12°, the lift–drag ratio of the airfoil with the blunt tail increased from +70% to −7.3% compared with the original airfoil, which shows that the airfoil with the blunt trailing edge has a better aerodynamic performance at a small angle of attack. The aerodynamic characteristics of the airfoil are affected by the periodic shedding of the wake vortex and also have periodic characteristics. By analyzing the vortex structure at the trailing edge, it was found that the value of K can affect the size of the vortex and the position of vortex generation/shedding. When α = 0°, α = 4°, and α = 8°, the blunt trailing edge could improve the aerodynamic performance of the airfoil; when α = 12°, the position of vortex generation changed, which reduced the aerodynamic performance of the airfoil. Therefore, when designing the trailing edge of an airfoil, the thickness of the trailing edge can be designed according to the specific working conditions. It can provide valuable information for the design and optimization of blunt trailing edge airfoil.