Parameter, input and state estimation for linear structural dynamics using parametric model order reduction and augmented Kalman filtering

Tracking the evolution in time of parameters, input and states of a structural dynamic system is often difficult, since their direct measurement can be problematic or even impossible. It is of great interest to estimate these quantities based on output-only data from a limited set of sensors. This work proposes an estimation technique for states, inputs and material parameters for structural dynamics models based on an Augmented Extended Kalman Filter. A parametric Model Order Reduction technique is proposed to construct a Reduced Order Model which maintains an explicit dependency on material parameters, enabling the parameter estimation thanks to a low computational cost and an efficient derivation of the linearized system. The choice of sensor configurations that ensure the observability of unknown quantities is discussed as well. The proposed methodology shows highly promising results and could be employed for model refinement or condition monitoring. The methodology is validated both numerically and experimentally, using data acquired on a scaled wind turbine blade, with errors on the estimated parameters lower than 3.5% with respect to experimentally identified parameter values.