A Neural Network Identification Technique for a Foil-Air Bearing and its Application to Unbalance Response Analysis

This paper proposes and studies the non-parametric system identification of a foil-air bearing (FAB) and its application to the frequency-domain nonlinear analysis of a foil-air bearing rotor system. This research is motivated by two advantages: (i) it removes computational limitations by replacing the air film and foil structure state equations by a displacement/force relationship; (ii) if the identification is based on empirical data, it can capture complications that cannot be easily modelled. A numerical model of the FAB is identified using a recurrent neural network (RNN). The training data sets are taken from the simultaneous time domain solution of the air film, foil and rotor equations. The RNN FAB model identified at a single speed is then validated over a range of speeds in two ways: (i) by subjecting it to several sets of input-output data that are different from those used in training; (ii) by using it in the harmonic balance (HB) solution process for the unbalance response of a rotor-bearing system. In either case, the test results using the identified model show good agreement with the exact results obtained using the air film and foil equations, demonstrating the great potential of this method, in the absence of self-excitation effects.