Distributed Fault Detection and Isolation of Large-scale Discrete-time Nonlinear Systems: an Adaptive Approximation Approach

摘要

This paper deals with the problem of designing a\uddistributed fault detection and isolation methodology for nonlinear\uduncertain large-scale discrete-time dynamical systems. As a\uddivide et impera approach is used to overcome the scalability issues\udof a centralized implementation, the large scale system being\udmonitored is modelled as the interconnection of several subsystems.\udThe subsystems are allowed to overlap, thus sharing some\udstate components. For each subsystem, a Local Fault Diagnoser is\uddesigned, based on the measured local state of the subsystem as\udwell as the transmitted variables of neighboring states that define\udthe subsystem interconnections. The local diagnostic decision is\udmade on the basis of the knowledge of the local subsystem dynamic\udmodel and of an adaptive approximation of the interconnection\udwith neighboring subsystems. The use of a specially-designed\udconsensus-based estimator is proposed in order to improve the\uddetectability and isolability of faults affecting variables shared\udamong overlapping subsystems. Theoretical results are provided\udto characterize the detection and isolation capabilities of the proposed\uddistributed scheme. Finally, simulation results are reported\udshowing the effectiveness of the proposed methodology.