Functional diagnosability and detectability of nonlinear models based on analytical redundancy relations. (November 2015) - Verdière, Nathalie; Jauberthie, Carine; Travé-Massuyès, Louise
Abstract : Highlights: We recall how to obtain specifics relations linking the outputs, inputs and the parameters (faults) of the model (ARRs). We propose a new definition of diagnosability based on residuals formed from ARRs, the functional diagnosability. We give a criteria for knowing if two faults intervening in same ARRs can be detected. We propose an example based on water tanks convincing.enough. Abstract: This paper introduces an original definition of diagnosability for nonlinear dynamical models called functional diagnosability . Fault diagnosability characterizes the faults that can be discriminated using the available sensors in a system. The functional diagnosability definition proposed in this paper is based on analytical redundancy relations obtained from differential algebra tools. Contrary to classical definitions, the study of functional diagnosability highlights some of the analytical redundancy relations properties related to the fault acting on the system. Additionally, it gives a criterion for detecting the faults. Interestingly, the proposed diagnosability definition is closely linked to the notion of identifiability, which establishes an unambiguous mapping between the parameters and the output trajectories of a model. This link allows us to provide a sufficient condition for testing functional diagnosability of a system. Numerical simulations attest the relevance of the suggested approach.