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European Control Conference, ECC 2003, Cambridge : United Kingdom (2003)
Fault diagnosis in nonlinear systems through an adaptive filter under a convex set representation
Manuel Adam Medina 1, Mickael Rodrigues 1, Didier Theilliol 1, Hicham Jamouli 2
(2003-09-01)

In this paper, the main goal is to design an approach that performs fault detection, isolation and estimation for a large class of nonlinear systems. Fault diagnosis is established by regarding system as a convex combination of linear time invariant (LTI) stochastic models and not as a single global model. The nonlinear representation is based on a bank of decoupled Kalman filters. This paper consists in generating a robust model selection of the “best” representative linear model. Under fault isolation conditions, the main contribution is to design an adaptive filter which makes possible multiple faults detection which appear simultaneously or in a sequential way, isolation and estimation over the whole operating range of nonlinear system. The stability conditions of the adaptive filter are developed. These conditions result in convex linear matrix inequalities (LMIs) that can be solved efficiently with optimization techniques. Performances of the method are tested on an academic example.
1:  Centre de recherche en automatique de Nancy (CRAN)
CNRS : UMR7039 – Université Henri Poincaré - Nancy I – Institut National Polytechnique de Lorraine (INPL)
2:  National School of Applied Sciences
University of Ibn Zohr, Agadir
Engineering Sciences/Automatic
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