  This paper deals with the problem of designing a distributed fault detection
and isolation algorithm for nonlinear large-scale systems that are subjected to
multiple fault modes. To solve this problem, a network of communicating
detection nodes is deployed to monitor the monolithic process. Each node
consists of an estimator with partial observation of the system's state. The
local estimator executes a distributed variation of the particle filtering
algorithm using the partial sensor measurements and the fault progression model
of the process. During the implementation of the algorithm, each node
communicates with its neighbors by sharing pre-processed information. The
communication topology is defined using graph theoretic tools. The information
fusion between the neighboring nodes is performed by means of a distributed
average consensus algorithm to ensure the agreement over the value of the local
likelihood functions. The proposed method enables online hypothesis testing
without the need of a bank of estimators. Numerical simulations demonstrate the
efficiency of the proposed approach.
