Robust control techniques have allowed engineers to create more descriptive models by including uncertainty in the form of additive noise and plant perturbations. As a result, the complete model set is robust to any discrepancies between the mathematical model and actual system. Experimental unfalsification of the model set leads to the guarantee that the model and uncertainties are able to recreate all experimental data points. In this work, such a robust control relevant model validation technique is applied to structural health monitoring in order to (1) detect the presence of damage and (2) identify the damage dynamics when used in conjunction with model-based identification. Additionally, the robust control relevant model validation technique allows for a novel quality measure of the identified damage dynamics. Feasibility of the method is demonstrated experimentally on a rotordynamic crack detection test rig with the detection and identification of a change in structure. Further insight is gained from application of the method to seeded damage on a rotor levitated on active magnetic bearings (AMBs) in the form of a local reduction in stiffness.