Authors:
Rafik Bensaadi
;
Hayet Mouss
and
Nadia Mouss
Affiliation:
Laboratoire d’Automatique et Productique, Université de Batna, Algeria
Keyword(s):
Diagnosis, fault detection, pattern recognition, fuzzy control, complex plant, conjugate gradients.
Related
Ontology
Subjects/Areas/Topics:
Applications of Expert Systems
;
Artificial Intelligence and Decision Support Systems
;
Enterprise Information Systems
Abstract:
In order to avoid catastrophic situations when the dynamics of a physical system (entity in a M.A.S architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems:
Identification of an actual state,
Identification of an eventual evolution towards a failure state,
Diagnosis and decision-making.