Nonlinear model identification of oil-lubricated tilting pad bearings

2015 ◽  
Vol 92 ◽  
pp. 533-543 ◽  
Author(s):  
Pouya Asgharifard-Sharabiani ◽  
Hamid Ahmadian
Keyword(s):  
Nonlinear Model ◽  
Model Identification ◽  
Tilting Pad ◽  
Tilting Pad Bearings ◽  
Nonlinear Model Identification

Nonlinear model identification of a frictional contact support

2010 ◽  
Vol 24 (8) ◽  
pp. 2844-2854 ◽  
Author(s):  
Hamid Ahmadian ◽  
Hassan Jalali ◽  
Fatemeh Pourahmadian
Keyword(s):  
Nonlinear Model ◽  
Frictional Contact ◽  
Model Identification ◽  
Nonlinear Model Identification

scholarly journals Nonlinear model identification and spectral submanifolds for multi-degree-of-freedom mechanical vibrations

2017 ◽  
Vol 473 (2202) ◽  
pp. 20160759 ◽  
Author(s):  
Robert Szalai ◽  
David Ehrhardt ◽  
George Haller
Keyword(s):  
Nonlinear Model ◽  
Invariant Manifolds ◽  
Model Identification ◽  
Oscillatory System ◽  
Least Square ◽  
Least Square Fit ◽  
Backbone Curves ◽  
Nonlinear Model Identification ◽  
Real Experimental Data ◽  
Spectral Submanifolds

In a nonlinear oscillatory system, spectral submanifolds (SSMs) are the smoothest invariant manifolds tangent to linear modal subspaces of an equilibrium. Amplitude–frequency plots of the dynamics on SSMs provide the classic backbone curves sought in experimental nonlinear model identification. We develop here, a methodology to compute analytically both the shape of SSMs and their corresponding backbone curves from a data-assimilating model fitted to experimental vibration signals. This model identification utilizes Taken’s delay-embedding theorem, as well as a least square fit to the Taylor expansion of the sampling map associated with that embedding. The SSMs are then constructed for the sampling map using the parametrization method for invariant manifolds, which assumes that the manifold is an embedding of, rather than a graph over, a spectral subspace. Using examples of both synthetic and real experimental data, we demonstrate that this approach reproduces backbone curves with high accuracy.

Using Genetic Programming in Nonlinear Model Identification

2012 ◽  
pp. 89-109 ◽  
Author(s):  
Stephan Winkler ◽  
Michael Affenzeller ◽  
Stefan Wagner ◽  
Gabriel Kronberger ◽  
Michael Kommenda
Keyword(s):  
Genetic Programming ◽  
Nonlinear Model ◽  
Model Identification ◽  
Nonlinear Model Identification
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