BP Network Model for Nonlinear Oil-film Force on Hydrodynamic Bearing
滑动轴承非线性油膜力的神经网络模型

The database method to compute nonlinear oil film force of the finite width hydrodynamic journal bearings can solve the contradiction between accuracy and efficiency, but the database and formulae of oil film force are subsections. Positions and velocities of bearings are synthesized into three basic parameters and state space transformation is used to change discontinuous oil film force databases to consecutive. So the integrative formula and BP network model based on consecutive databases forces are established to obtain oil film force under any movement states of axes with high accuracy. By means of computation example of cylinder journal hydrodynamic bearings, finite differential method, database method and BP neural network models of nonlinear oil film forces are employed to calculate oil film forces and orbit of shaft center in the transient analysis of the rotor bearing system. Results are shown that BP network model is more approximate to numerical computation methods and raises remarkably computational efficiency of bearings.