Nonlinear state observers and extended Kalman filters for battery systems

D-step ahead Kalman predictor for controlled autoregressive processes with random coefficients

Variable- and fixed-structure augmented interacting multiple-model algorithms for manoeuvring ship tracking based on new ship models

Fractional kalman filter algorithm for the states, parameters and order of fractional system estimation

Modeling of reliability characteristics typically assumes that components and systems fail if a certain individual damagelevel is exceeded. Every (mechanical) system damage increases irreversibly due to employed loading and (mechanical)stress, respectively. The main issue of damage estimation is adequate determination of the actual state-of-damage. Severalmathematical modeling approaches are known in the literature, focusing on the task of how loading effects damageprogression (e.g., W¨ohler, 1870) for wear processes. Those models are only valid for specific loading conditions, a prioriassumptions, set points, etc. This contribution proposes a general model, covering adequately the deterioration of a setof comparable systems under comparable loading. The main goal of this contribution is to derive the loading?damageconnection directly from observation without assuming any damage models at the outset. Moreover, the connection is notinvestigated in detail (e.g., to examine the changes in material, etc.) but only approximated with a probabilistic approach.The idea is subdivided into two phases: A problem-specific relation between loading applied (to a structure, which contributesto the stress) and failure is derived from simulation, where a probabilistic approach only assumes a distributionfunction. Subsequently, an adequate general model is set up to describe deterioration progression. The scheme will beshown through simulation-based results and can be used for estimation of the remaining useful life and predictive maintenance/control.Keywords: reliability, parameter estimation, damage accumulation, probabilistic simulation.

On the discrete time-varying JLQG problem

Optimal selection of measurement locations for parameter estimation in distributed processes

Distributed scheduling of sensor networks for identification of spatio-temporal processes