Distributed Fault Diagnosis of Nonlinear Stochastic Systems with Monitoring Networks

The design of a distributed fault detection and isolation filter is addressed for stochastic nonlinear systems subjected to multiple failure modes. The monolithic process is monitored by a network of communicating detection nodes, in lieu of a singleton, supervising diagnostic unit. Each detection node is a fault-sensitive estimator unit with processing capabilities and limited observation access to the process. The detection nodes exchange post-processed information over a communication network. An average consensus protocol is employed to fuse the detection nodes' outputs to obtain an agreement over the local likelihood functions. The proposed approach conducts online hypothesis testing without a bank of estimators, reducing the computational complexity of the algorithm significantly. Numerical simulations validate the efficiency of the proposed algorithm.