FAULT-DETECTION AND DIAGNOSIS IN DISTRIBUTED SYSTEMS - AN APPROACH BYPARTIALLY STOCHASTIC PETRI NETS

We address the problem of alarm correlation in large distributed syste ms. The key idea is to make use of the concurrence of events in order to separate and simplify the state estimation in a faulty system. Petr i nets and their causality semantics are used to model concurrency. Sp ecial partially stochastic Petri nets are developed, that establish so me kind of equivalence between concurrence and independence. The diagn osis problem is defined as the computation of the most likely history of the net given a sequence of observed alarms. Solutions are provided in four contexts, with a gradual complexity on the structure of obser vations.