Generació de Decisions davant d'Incerteses
Abstract
This thesis deals with the Fuzzy Inductive Reasoning (FIR) methodology
applied to fault detection and diagnosis systems. FIR, based on the
General Systems Problem Solver (GSPS) proposed by Klir in 1989, is a
methodological tool for data-driven construction of dynamical systems
and for studying their conceptual modes of behavior. FIR is a
qualitative modeling and simulation methodology that is based on
observation of the input-output behavior of the system to be modeled,
rather than on structural knowledge about its internal composition.
This methodology has evolved over time with the aim of enlarging the
class of problems that can be dealt with by FIR.
The work presented in this thesis aims to contribute to reducing
modeling and simulation efforts of real industrial complex systems.
Several methodological contributions have been made to increase FIR
robustness as well as to develop a new methodology to create robust and
efficient fault detection and diagnosis systems.
The main objective of this thesis is to reduce as much as possible the
sensitivity of the FIR methodology, by maximizing its robustness, in
such a way that it becomes a fundamental tool for developing efficient
fault detection and diagnosis systems.
The main contributions of this thesis are:
- To improve the robustness of FIR by creating a new tool,
Visual-FIR, that identifies patterns and predicts future behavior
of dynamical systems in a very efficient and simple to use
environment.
- To develop a new methodology for creating fault detection and
diagnosis systems based on FIR. We have developed a detection
technique, the enveloping, and a diagnosis measure, known as the
acceptability measure, that allow improving and making more
robust the fault detection and diagnosis processes of the FIRFDDS
(fault detection and diagnosis system based on FIR).
- To develop a tool that allows to easily create highly efficient
FIRFDDS for specific applications. A platform, named
VisualBlock-FIR, has been developed that allows the user to create,
in a simple way, fault detection and diagnosis systems based on
FIR.
In order to validate the methodological contributions and the developed
tools a couple of case studies have been presented in this
dissertation. The first corresponds to the benchmark problem of the
Damadics automatic valve system, which proposes four failures of small
and medium sizes that are detected and isolated / identified in a quick
and highly efficient way. The second is a simulated fuel cell where
five different faults are applied. The five faults are detected and
identified correctly. Finally, we check the robustness of the FIRFDDS
by adding white noise, at different magnitudes, to the outputs of the
fuel cell.
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Last modified: October 16, 2013 -- © François Cellier