Abstract
We deal with the problem of finding sets of observable events (event bases) that ensure language diagnosability of discrete-event systems modeled by finite state automata. We propose a methodology to obtain such event bases by exploiting the structure of the diagnoser automaton, and in particular of its indeterminate cycles. We use partial diagnosers, test diagnosers, and other new constructs to develop rules that guide the update of the observable event set towards achieving diagnosability. The contribution of this paper is the description of such rules and their integration into a set of algorithms that output minimal diagnosis bases.
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