Robust fault detection and isolation in bond graph modelled processes with Bayesian networks Online publication date: Tue, 14-Feb-2017
by Walid Bouallegue; Salma Bouslama; Moncef Tagina
International Journal of Computer Applications in Technology (IJCAT), Vol. 55, No. 1, 2017
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook