In this paper we formulate asynchronous diagnosis by means of hidden state history reconstruction, from
alarm observations. We follow a so-called true concurrency approach, in which no global state and no global time is available. Instead, we use only local states in combination with a partial order model of time, in which local events are ordered if they are either generated on the same site, or related via some causality relation. Our basic mathematical tool is that of net unfoldings originating from the Petri net research area. This study was motivated by the problem of event correlation in telecommunications network management.

This work is partially supported by RNRT (National Research Network in Telecommunication) through the MAGDA  project (Modelling and Learning for a Distributed Management of Alarms).