There are several sources of power loss of a roller bearing under operation. For example, the bearing material elastic hysteresis, the frictional moment generated by the differential sliding between the roller and the raceway, the friction moment between the roller and cage due to sliding, and the viscous friction moment caused by the rotation motion of the roller and cage in the lubricating oil, and so on. In addition to the SKF model, what other models can accurately estimate the power loss of bearings?
Zhifeng Shi, There are a number of models that can be used to estimate the power loss of bearings, including:
- SKF model: This model is widely used in the industry and is based on experimental data. It takes into account the various sources of power loss in bearings, including rolling friction, sliding friction, and viscous friction.
- Lundberg-Palmgren model: This model is a simplified version of the SKF model and is easier to use. It is based on the assumption that the power loss in bearings is due to rolling friction only.
- Harris model: This model is a more sophisticated version of the Lundberg-Palmgren model and takes into account the effects of sliding friction and viscous friction.
ISO model: This model is based on the international standard ISO 12081-2 and is designed to be used for a wide range of bearing types and operating conditions.
In addition to these models, there are a number of other models that have been developed to estimate the power loss of bearings under specific conditions. For example, there are models that have been developed to estimate the power loss of bearings in high-speed applications, in low-temperature applications, and in applications with different types of lubrication. The best model to use to estimate the power loss of bearings will depend on the specific application. For most applications, the SKF model is a good choice. However, for more demanding applications, such as high-speed applications or applications with extreme temperatures, a more sophisticated model may be required.
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