This paper aimed to reveal elastohydrodynamic lubrication characteristics for surface-textured slipper bearing of axial piston pump. The proposed model was established including slipper-tilting motion and surface deformation. Further, a numerical simulation was conducted under hydrodynamic and elastohydrodyamic lubrication conditions. Numerical solutions were obtained under this lubrication conditions in terms of the film pressure, film thicknesses, and bearing stiffness. The simulation results reveal that the film pressure in EHD solution increases slightly with the growth of the area density within the range of 10-30% and then tends to remain stable. When the area density of dimple is set to 38%, the dimensionless stiffness of oil film shows the highest value. If the dimple depth is set to 0.8 μm, there exists the maximum dimensionless stiffness of oil film in EHD solution when the optimum dimple depth-to-diameter ratio is set to 0.22.
Aiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon–Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, and processing of mechanical vibration signals. This paper presents a novel method of compressed sensing reconstruction for axial piston pump bearing vibration signals based on the adaptive sparse dictionary model. First, vibration signals were divided into blocks, and an energy sequence was produced in accordance with the energy of each signal block. Second, the energy sequence of each signal block was classified by the quantum particle swarm optimization algorithm. Finally, the reconstruction of machinery vibration signals was carried out using the K-SVD dictionary algorithm. The average relative error of the reconstructed signal obtained by the proposed algorithm is 4.25%, and the reconstruction time decreases by 43.6% when the compression ratio is 1.6.
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