Title: Robust fault detection and isolation in bond graph modelled processes with Bayesian networks

Authors: Walid Bouallegue; Salma Bouslama; Moncef Tagina

Addresses: University of Tunis ELMANAR, National Engineering School of Tunis, LR11ES20 Laboratoire d'Analyse, de Conception et de Commande des Systèmes, Tunis, Tunisia; Direction Générale des Etudes Technologiques, Institut Supérieur des Etudes Technologiques de Béja, Béja, Tunisia ' University of Tunis ELMANAR, National Engineering School of Tunis, LR11ES20 Laboratoire d'Analyse, de Conception et de Commande des Systèmes, Tunis, Tunisia ' Manouba University, National School of Computer Sciences, L3I Laboratory, Manouba, Tunisia

Abstract: The main objective of this paper is to present a new method for Fault Detection and Isolation (FDI) of non-linear uncertain parameters systems modelled by bond graphs (BGs) with Bayesian networks (BN). From the BG model of a process, residuals, which are fault detectors, are determined directly from the Diagnostic Bond Graph (DBG). In ideal conditions, those residuals are equal to zero. But in practice, owing to uncertainties, perturbations and measurement noises, residuals are different from zero. Classical approaches used thresholds to deduce whether a process is in normal operating mode or in faulty mode. In our approach, we generate a statistical decision procedure to detect the operating mode. For isolation, a Bayesian network is generated by covering the causal paths of the DBG, and the method proposed by Weber et al. is exploited. A simulation example on a three tanks system is provided to show the efficiency of the proposed FDI procedure.

Keywords: FDI; fault detection and isolation; modelling; bond graphs; parameter uncertainty; Bayesian networks; simulation; three tank systems.

DOI: 10.1504/IJCAT.2017.082261

International Journal of Computer Applications in Technology, 2017 Vol.55 No.1, pp.46 - 54

Received: 19 May 2015
Accepted: 11 Oct 2015

Published online: 14 Feb 2017 *

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