Methodology to knowledge discovery for fault diagnosis of hybrid dynamical systems: demonstration on two tanks system
1
Laboratoire d’Automatique et Informatique de Guelma (LAIG),
Université 8 Mai 1945 Guelma, BP 401, Guelma 24000, Algérie
2
Laboratory of Automatic Signal and Image Processing (LARATSI), National School of Engineers of Monastir, University of Monastir, 5019
3
Laboratory of Automatics and Informatics of Guelma LAIG
Submission date: 2020-07-26
Final revision date: 2020-11-12
Acceptance date: 2020-11-18
Online publication date: 2020-11-19
Publication date: 2020-11-19
Diagnostyka 2020;21(4):115–123
KEYWORDS
TOPICS
ABSTRACT
The work carried out in this article concerns on the implementation off a diagnostic procedure for hybrid dynamic systems (HDS) whose objective is to guarantee the proper functioning of industrial installations. In this context, the main contributions of this work are summarized into three parts: The first part is oriented to the modeling approach dedicated to HDS. The aim is to find an adequate model combining both aspects (continuous and discrete dynamics). The use of Neuro-fuzzy networks makes it possible to build a model of the system and to follow all the modes without it being necessary to identify or discern them. The second part concerns the synthesis of a fault diagnostic technique based on a fuzzy inference system. A Neuro-Fuzzy network based is used for residual generation, while for the residual evaluation, a fuzzy reasoning model is used which can mainly introduce heuristic information into the analysis scheme and takes the appropriate decision regarding the actual behaviour of the process. The proposed approach is successfully applied to monitoring faults of a non-linear three-tank system and the results confirm the effectiveness of this approach.
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