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The film pressure, film thickness and film stiffness are important parameters in study on water-lubricated bearings, which are difficult to be accurately measured due to the limitations of bearing structure and operating environment. Meanwhile, obtaining the empirical data can reflect the real bearing characteristics, and which is great significance to the further study of bearing lubrication and friction mechanism. In traditional monitoring system on water-lubricated bearing, the size of sensor is too large, and which is difficult to be embedded inside the bearing. In addition, the sensor installation position and the interference of external signal source will affect the system, so, there are large errors in test results. In order to solve the above problems, in this paper, an embedded intelligent bearing structure is proposed, in which a thin film pressure sensor is embedded inside the bearing bush to make it closer to signal source, and the collected data can reflect the bearing running state more accurately. At the same time, due to the integration of information processing units, the online monitoring, self-diagnosis and automatic control can be realized, and the operation of bearing is more stable and smart than before. The physical model after grooving was established by two-way fluid-structure coupling method, the finite element simulation, and the analysis of bearing deformation and capacity were conducted. The results show that the bearing mechanical characteristics are affected by the grooving parameters, when the groove depth increases, the bearing stress and deformation also increases greater accordingly. The deformation of bearing bush decreases along the axial direction, and the bearing stress near the groove is larger.
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