Dynamic analysis of hydrodynamic bearing–rotor system based on neural network

Abstract

Feed-forward neural network is employed to model the nonlinear oil-film force database of a finite-length hydrodynamic journal bearing, which is constructed by continuous transformation of Reynolds equation. Neural network models trained are utilized to investigate motion characteristics of a rigid unbalanced rotor supported on elliptical bearings in 300 MW steam turbine generator set. There exist various forms of periodic, quasi-periodic and chaotic motions at different rotating speeds. Periodic doubling bifurcation and quasi-periodic routes to chaos may be found when rotating speed is used as the control parameter. Computational results show that there exist similar motion behaviors between neural networks and numerical method. It is available for neural network models of oil-film forces to research nonlinear dynamic problems of rotating machinery.