Event-Triggered Robust Fault Diagnosis Kalman Filter for Stochastic Systems

Abstract

This paper proposes a new method for robust fault diagnosis based on data-sending management in discrete-time linear stochastic systems. First, a methodology for identifying a subspace independent of disturbances is presented, which significantly attenuates the effects of disturbances. The sufficient conditions are proposed as linear matrix inequalities (LMIs) to guarantee the observability and stability of a new subspace. After that, the recursive robust Kalman filter is extended based on data-sending management. Finally, this Kalman filter is used to design residual signals. Event-triggered and self-triggered hybrid methods are used to decrease the transfer amount of measured data, and at the same time to preserve the fault diagnosis method performance. Simulation results illustrate the capabilities and effectiveness of the proposed design methodology for fault diagnosis in linear stochastic systems with unknown inputs while the data-sending rate decreases significantly.