Dynamic Analysis and Machine Learning Prediction of a Nonuniform Slot Air Bearing System

Abstract

Abstract Nonuniform slot air bearing (NSAB) systems have two major advantages, the external air supply and slot restrictor design, and their inherent multidirectional supporting forces and stiffness that provide excellent rotational stability. However, NSAB systems are prone to vibration from nonperiodic or chaotic motion caused by nonlinear pressure distribution within the gas film, gas supply imbalance, or simply inappropriate design. It is necessary to determine under which conditions these nonperiodic motions arise, and to design a NSAB system that will resist these vibrations. The dynamic behavior of a rotor supported by an NSAB system was studied using spectral response, bifurcation, Poincaré map, and the maximum Lyapunov exponent. The numerical results showed that chaos in an NSAB system occurred within specific ranges of rotor mass and bearing number. For example, the chaotic regions where the maximum Lyapunov exponents were greater than zero occurred in the intervals of rotor mass 20.84 ≦ mf < 24.1 kg with a bearing number of Λ = 3.45. In addition, the coupling effect of rotor mass and bearing number was also investigated. To predict chaotic behavior, ensemble regression, and the back propagation neural network were used to forecast the occurrence of chaos. It was found that ensemble regression using dataset of 26 × 121 gave the best results and most accurate prediction for this NSAB system. The results may make a valuable contribution to the design of NSAB systems for use in a wide variety of industrial applications.