Fair diagnosability in PN-based DES models

Abstract

Failure diagnosability has been widely studied for discrete event system (DES) models because of modeling simplicity and computational efficiency due to abstraction. Frameworks based on FSMs, process algebra, Petri nets (PN) etc. have been used for modeling and diagnosability analysis of DES. DES failure diagnosability algorithms work successfully for systems where fairness is not a part of the model. They are based on detecting cycles in the normal and the failure model that look identical. However, there exist systems with all transitions fair where the diagnosability condition that hinges upon this feature renders many failures non-diagnosable although they may actually be diagnosable by transitions out of a cycle. Hence, the diagnosability conditions based on cycle detection need to be modified to hold for many real-world systems where all transitions are fair. In this paper a new failure diagnosability mechanism is proposed for PN based DES models with fair transitions.