Authors:
Nourhène Ben Rabah
1
;
Ramla Saddem
2
;
Faten Ben Hmida
3
;
Véronique Carre-Menetrier
2
and
Moncef Tagina
3
Affiliations:
1
URCA and University of Manouba, France
;
2
URCA, France
;
3
University of Manouba, Tunisia
Keyword(s):
Discrete Event Systems, Automated Production System, Causal Temporal Signatures, Simulation, Faults Diagnosis, Learning, Similarity.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Causal Temporal Signatures (CTS) is an efficient formalism for behaviors description and recognition of fault diagnosis in Discrete Event Systems (DES). The main advantages of this formalism are the readability and the expressivity. Indeed, it is able to describe clearly all desired behaviors and it is understandable and readable by an expert in the field. However, it raises the problem of acquisition and updating of expert knowledge stored in a CTS base. In this paper, we suggest an incremental learning approach based on the simulation to acquire and update automatically a consistent CTS base. The proposed approach is illustrated with an example applied to the turntable helps to understand the different modules of the method.