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

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-01230242
Contributor : David Gucik-Derigny <>
Submitted on : Wednesday, November 18, 2015 - 10:15:40 AM
Last modification on : Monday, March 30, 2020 - 8:43:16 AM

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David Gucik-Derigny, Rachid Outbib, Mustapha Ouladsine. A Comparative Study of Unknown-Input Observers for Prognosis Applied to an Electromechanical System. IEEE Transactions on Reliability, Institute of Electrical and Electronics Engineers, 2015, PP (99), http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7328777&filter%3DAND%28p_IS_Number%3A4378406%29. ⟨10.1109/TR.2015.2494682⟩. ⟨hal-01230242⟩

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