Grease life refers to the time it takes for the grease to lose its ability to keep the lubrication due to grease degradation. As grease life is generally shorter than fatigue life of bearing, the service life of greas...Grease life refers to the time it takes for the grease to lose its ability to keep the lubrication due to grease degradation. As grease life is generally shorter than fatigue life of bearing, the service life of grease-lubricated rolling bearings is often dominated by grease life. When designing a bearing systemolecular weightith grease lubrication, it is necessary to define the operating conditions limits of the bearing, for which grease life becomes a determining factor. Prolongation of grease life becomes an especially important challenge when the bearing is to be operated trader high-speed, high-temperature, and other severe conditions. Selecting a number of commercially sold greases composed of varying base oils, the author evaluated their properties and analyzed how each property affected the grease life by performing a multiple regression analysis. The optimum grease composition to best exploit each property was also examined. The results revealed among others that one would need to first determine the base oil type and then maximize ultimate bleeding while minimizing the evaporation rate.展开更多
A Kalman filter which estimates unsteady laminar flow in a pipe is implemented on a real-time computing system. The plant model is the optimised finite element model of pipeline dynamics considering unsteady laminar f...A Kalman filter which estimates unsteady laminar flow in a pipe is implemented on a real-time computing system. The plant model is the optimised finite element model of pipeline dynamics considering unsteady laminar friction. A steady-state Kalman filter is built based on the model of pipeline dynamics. Pressure signals at both ends of a target section of a pipe are input to the model of pipeline dynamics, and as an output of the model an estimated pressure signal at a mid-point of the pipe is obtained. Difference between measured and estimated pressure signals at the mid-point is fed back to the model of pipeline dynamics to modify state variables of the model. According to the Kalman filter principle, the state variables of the model are adjusted so that they converge to real values. It is demonstrated that real-time implementation of the Kalman filter is possible with the sampling time of 0.1 ms.展开更多
文摘Grease life refers to the time it takes for the grease to lose its ability to keep the lubrication due to grease degradation. As grease life is generally shorter than fatigue life of bearing, the service life of grease-lubricated rolling bearings is often dominated by grease life. When designing a bearing systemolecular weightith grease lubrication, it is necessary to define the operating conditions limits of the bearing, for which grease life becomes a determining factor. Prolongation of grease life becomes an especially important challenge when the bearing is to be operated trader high-speed, high-temperature, and other severe conditions. Selecting a number of commercially sold greases composed of varying base oils, the author evaluated their properties and analyzed how each property affected the grease life by performing a multiple regression analysis. The optimum grease composition to best exploit each property was also examined. The results revealed among others that one would need to first determine the base oil type and then maximize ultimate bleeding while minimizing the evaporation rate.
文摘A Kalman filter which estimates unsteady laminar flow in a pipe is implemented on a real-time computing system. The plant model is the optimised finite element model of pipeline dynamics considering unsteady laminar friction. A steady-state Kalman filter is built based on the model of pipeline dynamics. Pressure signals at both ends of a target section of a pipe are input to the model of pipeline dynamics, and as an output of the model an estimated pressure signal at a mid-point of the pipe is obtained. Difference between measured and estimated pressure signals at the mid-point is fed back to the model of pipeline dynamics to modify state variables of the model. According to the Kalman filter principle, the state variables of the model are adjusted so that they converge to real values. It is demonstrated that real-time implementation of the Kalman filter is possible with the sampling time of 0.1 ms.
