Nonlinear Identification of an Aero-Engine Component Using Polynomial Nonlinear State Space Model

Abstract

In non-linear structural dynamics, the identification of nonlinearity often requires prior knowledge or an initial assumption of the mathematical law (model) of the type of nonlinearity present in a system. However, applying such assumptions to large structures with several sources and types of nonlinearities can be difficult or practically impossible due to the individualistic nature of nonlinear systems. This paper presents the identification of an aerospace component using polynomial nonlinear state space models. As a first step, the best linear approximation (BLA), noise and nonlinear distortion levels are estimated over different amplitudes of excitation. Next, a linear state space model is estimated on the nonparametric BLA using the frequency domain subspace identification method. The nonlinear model is constructed using a set of multivariate polynomial terms in the state variables and the parameters are estimated through a nonlinear optimisation routine. The polynomial nonlinear state space models are tested and validated on measured data obtained from the experimental investigation of the Aero-Engine component.