Authors:
Yousra Hafidi
1
;
Laid Kahloul
2
and
Mohamed Khalgui
3
Affiliations:
1
School of Electrical and Information Engineering, Jinan University, China, LISI Laboratory, National Institute of Applied Sciences and Technology, University of Carthage, Tunis 1080, Tunisia, LINFI Laboratory, Computer Science Department, Biskra University, Algeria, University of Tunis El Manar, Tunis and Tunisia
;
2
LINFI Laboratory, Computer Science Department, Biskra University and Algeria
;
3
School of Electrical and Information Engineering, Jinan University, China, LISI Laboratory, National Institute of Applied Sciences and Technology, University of Carthage, Tunis 1080 and Tunisia
Keyword(s):
Reconfigurable Systems, Modeling and Verification, Petri Net, Backward Reachability, Model-base Diagnosis.
Related
Ontology
Subjects/Areas/Topics:
Formal Methods
;
Simulation and Modeling
;
Software Engineering
;
Software Engineering Methods and Techniques
Abstract:
This paper deals with reconfigurable discrete event control systems (RDECSs). We model RDECSs using reconfigurable timed net condition/event systems (R-TNCESs) formalism which is an extension from Petri nets to deal with reconfiguration properties. Model-based diagnosis algorithms are widely used in academia and industry to detect faulty components and ensure systems safety. The application of these methods on reconfigurable systems is impossible due to their special behavior. In this paper, we propose accomplishing techniques of backward reachability to make reconfigurable systems model-based diagnosis possible using R-TNCESs. The flexibility among reconfigurable systems like RDECSs allows them to challenge recent requirements of markets. However, such properties and complicated behavior make their verification task being complex and sometimes impossible. We deal with the previous problem by proposing a new methodology based on backward reachability of RDECSs using (R-TNCESs) formal
ism including improvement methods. The proposed methodology serves to reduce as much as possible redundant computations and gives a package to be used in model-based diagnosis algorithms. The paper’s contribution is applied to a benchmark modular production system. Finally, a performance evaluation is achieved for different sizes of the problem to study benefits and limits of the proposed methodology among large-scale systems.
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