A Comparative Study of Unknown-Input Observers for Prognosis Applied to an Electromechanical System

In this paper, a contribution to solve the system prognostic problem is proposed. For that, the concept is defined in this work as a problem of predictive diagnosis under temporal constraint. Generally, this problem is treated using mainly approaches that are based on dynamic systems, experts' knowledge or are data-driven. Here, in order to describe the behavior of a process, we consider dynamic models that are composed of differential equations. The goal of this work is twofold. First, we present a new strategy for system prognosis based on observer design. Second, we propose a comparative study of two methodologies, dedicated to observer design, with application to an electromechanical process. To illustrate the performances of the approaches, simulation results are proposed.