Skip to Main Content
In this paper we study fault detection in systems that can be modeled as finite state machines (FSMs). We aim at detecting faults that manifest themselves as permanent changes in the next-state transition functionality of the FSM. Fault diagnosis is performed by an external observer/diagnoser that functions as an FSM and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the system at irregular time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free.