Authors:
Moamar Sayed Mouchaweh
;
Alexandre Phillipot
and
Véronique Carré Ménétrier
Affiliation:
Université de Reims, CReSTIC - LAM, France
Keyword(s):
Discrete Event Systems, Modelling, Diagnosis, Manufacturing systems.
Related
Ontology
Subjects/Areas/Topics:
Discrete Event Systems
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
The diagnosis is defined as the process of detecting an abnormality in the system behavior and isolating its causes or sources. Not all the systems are diagnosable. Thus, before Appling a method to diagnose a system, we need to know if this system is diagnosable according to the set of failures required to be detected and isolated. This paper presents an algorithm to determine if a system is detectable or not, i.e., if we can know, at each instant, whether the system works under a normal or abnormal functioning state. In the case that the system is detectable, this algorithm determines if this system is diagnosable. This algorithm combines event and state based approaches in order to maximise the diagnosability power with a minimum number of sensors. In addition, the time is integrated and modelled with fuzzy intervals to enhance this diagnosabilty power and to take into account the imprecision of events occurrences instants. An example of manufacturing system is used to illustrate t
he functioning of this algorithm.
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