Among numerous damage identification techniques, those which are used for online data-driven damage identification have received considerable attention recently. One of the most widely-used vibration-based time-domain techniques for nonlinear system identification is the extended Kalman filter, which exhibits a good performance when the parameter to be identified is a constant parameter. However, it is not as successful in identification of changes in time-varying system parameters, which is essential for real-time identification. Alternatively, the extended Kalman-Bucy filter has been recently proposed due to its enhanced capabilities in parameter estimation compared with extended Kalman filter. On the other hand, when applied as the excitation in some attractor-based damage identification techniques, chaotic and hyperchaotic dynamics produce better outcomes than does common stochastic white noise. The current study combines hyperchaotic excitations and the enhanced capabilities of extended Kalman-Bucy filter to propose a real-time approach for identification of damage in nonlinear structures. Simulation results show that the proposed approach is capable of online identification and assessment of damage in nonlinear elastic and hysteretic structures with single or multiple degrees-of-freedom using noise-corrupted measured acceleration response. © The Society for Experimental Mechanics 2014.

© 2015 Springer Science+Business Media Dordrecht A real-time damage identification approach using an extended Kalman–Bucy filter is proposed for identification and real-time monitoring of damage-induced changes which is applicable both as a vibration-based technique and as a guided-wave technique for structural health monitoring. The proposed approach makes use of the intrinsic hypersensitivity of hyperchaotic systems to subtle changes in system parameters by applying the augmented state method along with an optimal filtering problem to provide simultaneous estimation of the states and parameters of a (possibly) nonlinear structural system driven by a tuned hyperchaotic excitation in order to monitor damage-induced changes. The proposed approach can also be employed for guided-wave structural health monitoring by discretizing the governing partial differential equation to obtain a process model in the context of optimal filtering problem, and thus to estimate the transmitted hyperchaotic wave and model parameters. Numerical simulation shows that the proposed approach is capable of real-time identification of reduction in elastic modulus of an isotropic beam.