Abstract: The main objective developed in this paper is to find a
graphic technique for modeling, simulation and diagnosis of the
industrial systems. This importance is much apparent when it is about
a complex system such as the nuclear reactor with pressurized water
of several form with various several non-linearity and time scales. In
this case the analytical approach is heavy and does not give a fast
idea on the evolution of the system. The tool Bond Graph enabled us
to transform the analytical model into graphic model and the
software of simulation SYMBOLS 2000 specific to the Bond Graphs
made it possible to validate and have the results given by the
technical specifications. We introduce the analysis of the problem
involved in the faults localization and identification in the complex
industrial processes. We propose a method of fault detection applied
to the diagnosis and to determine the gravity of a detected fault. We
show the possibilities of application of the new diagnosis approaches
to the complex system control. The industrial systems became
increasingly complex with the faults diagnosis procedures in the
physical systems prove to become very complex as soon as the
systems considered are not elementary any more. Indeed, in front of
this complexity, we chose to make recourse to Fault Detection and
Isolation method (FDI) by the analysis of the problem of its control
and to conceive a reliable system of diagnosis making it possible to
apprehend the complex dynamic systems spatially distributed applied
to the standard pressurized water nuclear reactor.
Abstract: Bond Graph as a unified multidisciplinary tool is widely
used not only for dynamic modelling but also for Fault Detection and
Isolation because of its structural and causal proprieties. A binary
Fault Signature Matrix is systematically generated but to make the
final binary decision is not always feasible because of the problems
revealed by such method. The purpose of this paper is introducing a
methodology for the improvement of the classical binary method of
decision-making, so that the unknown and identical failure signatures
can be treated to improve the robustness. This approach consists of
associating the evaluated residuals and the components reliability data
to build a Hybrid Bayesian Network. This network is used in two
distinct inference procedures: one for the continuous part and the
other for the discrete part. The continuous nodes of the network are
the prior probabilities of the components failures, which are used by
the inference procedure on the discrete part to compute the posterior
probabilities of the failures. The developed methodology is applied
to a real steam generator pilot process.