Fault Diagnosis of Discrete Event Systems Modeled by Time-Interval Automaton

We address in this paper the problem of diagnosability of time-interval discrete event systems (TIDES), a class of discrete event systems that has a single clock structure and whose event occurrences take place within a time interval after the previous event occurrence. The idea behind the diagnosability of TIDES is to leverage time information to distinguish fault traces from non-faulty ones, increasing the accuracy of the fault diagnosis system. For this purpose, we use a recently proposed timed model called time-interval automaton (TIA), and, based on this model, we present necessary and sufficient conditions for the diagnosability of discrete event systems modeled by TIA and an algorithm for its verification. Examples illustrate all of the results presented in the paper. Note to Practitioners—Unlike the usual assumption on discrete-event systems, whose evolution is dictated by the asynchronous occurrence of events, in practice, there is always some knowledge on the time associated with event occurrence; e.g., in a manufacturing system, it is also possible to have an estimation on the time the parts take to go from the beginning of a conveyor belt up to different sensors located along the conveyor. In this paper, the time information is given by the time interval within which an event must occur after the system reaches some state, and we take advantage of this information to improve the accuracy of the fault diagnosis system.