Nonlinear system identification using empirical slow flow model

Abstract

Most of the existing SHM damage detection methodologies are based on the changes in the system parameters estimated by the linear model fitted to the structure. However, real-life structures exhibit nonlinearity even in their healthy state due to complex joints and interfaces etc. Nonlinear identification and damage detection is a very challenging inverse engineering problem. In this paper, we present a nonlinear system identification methodology using empirical slow flow model. This nonlinear system identification technique using empirical slow flow model requires only input-output measurement and need not know about the type of nonlinearity present in the system. Numerical simulation studies have been conducted on Duffing oscillator and a beam with breathing crack to demonstrate the effectiveness of the proposed algorithm.