Fault detection and diagnosis relying on set membership identification for time varying systems

In this paper, a Fault Detection and Diagnosis (FDD) method relying on Set Membership Identification (SMI) is presented, aiming at the detection of multiple abrupt parameter variations for a time varying system. The proposed method utilizes a jump linearly parametrizable model, assuming unknown but bounded measurement noise and parameter perturbations. The objective of SMI is to compute at every time instant the orthotope containing the nominal parameter vector, while a fault is detected at the time instant that this orthotope is empty. In order to proceed to fault isolation and identification, an update of the SMI procedure is realized. The fault isolation is based on the orthotopes' projections, whose centers are used for fault identification. Simulations studies are used to verify the efficiency of the suggested method applied in an Atomic Force Microscope.