This paper proposes and studies the nonparametric system identification of a foil-air bearing (FAB). This research is motivated by two advantages: (a) it removes computational limitations by replacing the air film and foil structure equations by a displacement/force relationship and (b) it can capture complications that cannot be easily modeled, if the identification is based on empirical data. A recurrent neural network (RNN) is trained to identify the full numerical model of a FAB over a wide range of speeds. The variable-speed RNN-FAB model is then successfully validated against benchmark results in two ways: (i) by subjecting it to different input data sets and (ii) by using it in the harmonic balance (HB) solution process for the unbalance response of a rotor-bearing system. In either case, the results from the identified variable-speed RNN maintain very good correlation with the benchmark over a wide range of speeds, in contrast to an earlier identified constant-speed RNN, demonstrating the great potential of this method in the absence of self-excitation effects.
A Neural Network Identification Technique for a Foil-Air Bearing Under Variable Speed Conditions and Its Application to Unbalance Response Analysis
University of Manchester,
Pariser Building, Sackville Street,
Manchester M13 9PL, UK
Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received September 2, 2015; final manuscript received April 12, 2016; published online August 11, 2016. Assoc. Editor: Daejong Kim.
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Hassan, M. F. B., and Bonello, P. (August 11, 2016). "A Neural Network Identification Technique for a Foil-Air Bearing Under Variable Speed Conditions and Its Application to Unbalance Response Analysis." ASME. J. Tribol. March 2017; 139(2): 021501. https://doi.org/10.1115/1.4033455
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