Distributed fault detection via particle filtering and decision fusion

Due to the growing demands for system reliability and availability of large amounts of data, efficient fault detection techniques are desired. In this paper, we consider nonlinear, non-Gaussian systems monitored by multiple sensors. Normal and faulty behaviors can be modelled as two hypotheses. Due to the communication constraints, it is assumed that sensors can only send binary data to the fusion center. Under the assumption of independent, identically distributed observations, we propose a distributed fault detection algorithm, including local detector design and decision fusion rule design, based on the state estimation by particle filtering. Experimental results show the efficiency of our proposed algorithm and its superiority over the conventional Kalman filter-based methods.