Reduced-Order Distributed Fault Diagnosis for Large-Scale Nonlinear Stochastic Systems

This paper deals with the distributed fault detection and isolation problem of uncertain, nonlinear large-scale systems. The proposed method targets applications where the computation requirements of a full-order failure-sensitive filter would be prohibitively demanding. The original process is subdivided into low-order interconnected subsystems with, possibly, overlapping states. A network of diagnostic units is deployed to monitor, in a distributed manner, the low-order subsystems. Each diagnostic unit has access to a local and noisy measurement of its assigned subsystem's state, and to processed statistical information from its neighboring nodes. The diagnostic algorithm outputs a filtered estimate of the system's state and a measure of statistical confidence for every fault mode. The layout of the distributed failure-sensitive filter achieves significant overall complexity reduction and design flexibility in both the computational and communication requirements of the monitoring network. Simulation results demonstrate the efficiency of the proposed approach.