Checking the diagnosability of a discrete event system aims at determining whether a fault can always be identified with certainty after the observation of a bounded number of events. This paper investigates the problem of pattern diagnosability of systems modeled as bounded labeled prioritized Petri nets that extends the diagnosability problem on single fault events to more complex behaviors. An effective method to automatically analyze the diagnosability of a pattern is proposed. It relies on a specific Petri net product that turns the pattern diagnosability problem into a model-checking problem.

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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.