Timed Diagnosability Analysis Based on Chronicles

Abstract Automated chronicle recognition is an efficient and robust method for fault diagnosis in timed discrete-event systems (TDES). This paper addresses the problem of diagnosability of TDES with regards to such a diagnosis method. We propose a fully automated chain to a priori check whether faults can be identified with certainty based on a given set of chronicles. To deal with the time aspects inherent to the chronicles, we first propose an automated translation of chronicles into a set of Labeled Time Petri Nets with Priorities. The diagnosability analysis is then performed on the state class graph of these nets and consists in determining whether the recognition of a chronicle is exclusive or not.