FINITE MEMORY OBSERVER FOR SWITCHING SYSTEMS: APPLICATION TO DIAGNOSIS

Abstract

In this paper, a fault detection method is developed for switching dynamic systems with unknown inputs. These systems are represented by several linear models, each of them being associated to a particular operating mode. The proposed method is based on Finite Memory Observers and mode probabilities with the aim of finding the system operating mode and estimating the unknown input. The method also uses a priori knowledge information about the mode transition probabilities represented by a Markov chain. The proposed algorithm is of supervised nature where the faults to be detected are a priori indexed and modelled. In this work, the method is applied for the fault detection of a linear system characterized by a model of normal operating mode and several fault models. Then, it applied 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 shows the validity and some advantages of the suggested method.