AN ADAPTIVE CHANGE DETECTION SCHEME FOR A NONLINEAR BEAM MODEL

In this paper, we consider parameter estimation techniques for detecti ng changes of a nonlinear nature in an Euler-Bernoulli beam model. The nonlinear stiffness used provides a very simple model of damage, and the adaptive estimation algorithm is used to track the onset of the no nlinearity. Using Lyapunov redesign methods, extended and applied to i nfinite dimensional systems, a stable learning scheme is developed. Th e resulting parameter adaptation rule is able to ''sense'' the instanc e of the fault occurrence. In addition, it identifies the location and the shape of the fault where the beam is persistently excited. Simula tion studies are used to illustrate the applicability of the theoretic al results.