Probabilistic fault diagnosis in discrete event systems

This paper presents a concept of discrete-event probabilistic fault diagnosis as an extension of the binary decision approach proposed by Sampath et al., where unobservable failure events are included in the representation of the system behavior under both normal and faulty conditions. It is assumed that the probability of each transition is known at the time of decision making. Based on this finite-state automaton model, probabilistic reasoning is applied for on-line diagnosis of dynamical systems. The major advantage of this approach is early detection of multi-component faults, which facilitates robust reconfiguration of the decision and control system.