Robust Fault Detection Performances for Stochastic Systems based on Adaptive Threshold

This paper investigates the problem of fault detection and isolation for discrete linear systems subjected to unknown disturbances, actuator and sensor faults. A bank of Robust Two Stage Kalman filters 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 allow the detection of low magnitude faults as fast as possible with a minimum risk of errors, the increase of detection probability and the reduction of false alarm probability.