A Particle-Filtering Based Approach for Distributed Fault Diagnosis of Large-Scale Interconnected Nonlinear Systems
For engineers monitoring large-scale interconnected nonlinear systems, this work provides a distributed method for fault diagnosis that reduces communication overhead and eliminates the need for multiple estimators.
This paper proposes a distributed fault detection and isolation algorithm for nonlinear large-scale systems using a particle filtering approach with consensus-based information fusion, enabling online hypothesis testing without a bank of estimators. Numerical simulations demonstrate the efficiency of the approach.
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.