Finite memory observer for switching system: application to diagnosis

Abdelfettah Hocine (1) , Didier Maquin (1) , José Ragot (1)

1 CRAN - Centre de Recherche en Automatique de Nancy

In this paper, we develop a fault detection method for switching dynamic systems with unknown inputs. These systems are represented by several linear models, each of them being associated to a particular operation mode. The proposed method is based on the use of Finite Memory Observers and mode probabilities with the aim to finding the operating mode of the system and estimating the unknown input. The resulting method also uses the knowledge of a priori information on the mode transition probabilities represented by a Markov chain. The proposed algorithm belongs to the class of the supervised algorithms where the fault to be detected are a priori indexed and modelled. First, the method is used for fault detection in the case of a linear system characterized by a normal model of operation and several fault models. Then, it applies for fault detection in the case of a linear system with unknown input where state and unknown input estimation are done simultaneously. A comparison with the Generalized Pseudo- Bayesian method is carry out showing the advantages of the suggested method.

Keywords: Diagnosis; switching system; multiple model; finite memory observer; unknown input; state estimation

Domains: Automatic

Abdelfettah Hocine, Didier Maquin, José Ragot. Finite memory observer for switching system: application to diagnosis. Workshop on Advanced Control and Diagnosis, Dec 2004, Karlsruhe, Germany. pp.CDROM. ⟨hal-00151291⟩