Hybrid Algorithm for Structural Health Monitoring of High-Rate Systems

Abstract

Engineering systems subject to high-rate extreme environments can often experience a sudden plastic deformation during a dynamic event. Examples of such systems include civil structures exposed to blast or aerial vehicles experiencing impacts. The change in configuration through deformation can rapidly lead to catastrophic failures resulting in intolerable losses in investments or human lives. A solution is to conduct fast system estimation enabling real-time decisions, in the order of microseconds, to mitigate such high-rate changes. To do so, we propose a model-driven observer coupled with a data-driven adaptive wavelet neural network to provide real-time stiffness estimations to continuously update a system’s model. This real-time system identification method offers adaptability of the system’s parameters to unforeseeable changes. The results of the simulations demonstrate accurate stiffness estimations in milliseconds for three different excitation conditions for a one degree-of-freedom spring, mass, and damper system with variable stiffness.