An experimental demonstration of tailored excitations for improved damage detection in the presence of operational variability

We have shown in previous work that the parameters governing the dynamic evolution of a system of ordinary differential equations may be modified via an evolutionary algorithm to yield excitations that improve damage detection sensitivity in a computational model of a structural health monitoring application. In this work we use the same method to develop improved excitations for an experimental system. Improvement in change detection sensitivity is shown for several generated excitations. In addition, we present an excitation that increases the robustness of the damage detection feature to the types of parameter perturbations that arise not from damage, but from environmental sources such as temperature change.