Name	Distributed and incremental diagnosis of discrete-event systems
Theme	Diagnosis of large scale discrete event systems
Begin	2000
State	Ongoing
Description	One of the main difficulties of discrete event system modeling is the intractable size of the model and the huge number of states and trajectories to be explored. To cope with this problem, we proposed to use a decentralized approach [3] which allows us to compute on-line diagnosis without requiring the computation of the global model. Given a decentralized model of the system and a flow of observations, the program computes the diagnosis by combining local diagnoses built from local models (or local diagnosers). In real systems, generally the observed events do not exactly correspond to the emitted events. Thus, instead of only considering partially ordered observations (as in [3]), we proposed to represent the uncertainty on emitted observations by an automaton and extended the decentralized approach to cope with this new representation. In order to deal with on-line systems, we then proposed to slice the observation flow into temporal windows, introduced the concept of automata chains to represent the successive observation slices, and proposed an algorithm to compute the diagnosis in an incremental way from these diagnosis slices [61]. More recently, we defined two independence properties (transition and state independence) and we showed their relevance to get a tractable representation of diagnosis [10]. The diagnosis slices are economically represented by a set of transition-independent diagnoses and its associated set of abstract descriptions, from which the set of final states and the trajectories of the global diagnosis can be easily retrieved.