Robust fault detection and accommodation for stochastic systems based on adaptive threshold

This paper treats the problem of sensor and actuator fault detection and accommodation for a linear stochastic systems subjected to unknown disturbances. A bank of augmented robust three-stage Kalman filters (ARThSKF) is adapted to estimate both the state and the fault as well as to generate the residuals. Besides, this paper presents the evaluation of the residuals with Bayes test of binary hypothesis test for fault detection to adaptive threshold compared with fixed threshold. This test allows the detection of low magnitude faults as fast as possible with a minimum risk of errors, which reduces the probability of non-detection and false alarm probability. Moreover, the result given by the fault detection and diagnosis part are then used by the fault accommodation (FA) that tolerates the faults and compensates its effects on the system behaviour.