Decentralized Fault Detection and Diagnosis in Large Linear Stochastic Systems

摘要

Due to the informational constraints, the computation and implementation of the Decentralized Fault Detection and Diagnosis (DFDD) algorithms are very complex. In this paper, we propose a new method for DFDD in large linear stochastic dynamical systems using an unknown input observer estimator scheme. We show that the Kalman filter#x2014;Genaralized Likelihood Ratio (GLR) test combination with our decentralized observer#x2014;estimator is similar to the approach of Willsky and Jones in centralized situations.