Distributed fault diagnosis with overlapping decompositions and consensus filters

This paper deals with the problem of building a distributed fault detection architecture for large-scale dynamical systems that are modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem a local fault detector is built, such that it can measure the local state of its subsystem as well as receive through communication links a measure of the neighboring states. The local detection decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptively learned approximation of the interconnection with neighbouring subsystems. The use of a specially-designed consensus filter is proposed in order to improve the capability of the diagnosers to detect faults affecting variables shared among different subsystems. Simulation results give an evidence of the effectiveness of the proposed fault detection scheme and of the use of overlapping decompositions and consensus filters.