Fault detection and isolation for linear discrete-time systems using input/output measurement analysis

In this work, a new approach to fault detection and isolation is proposed for linear discrete-time systems subject to faults and bounded additive disturbances using a system model, together with input/output measurements over a finite estimation horizon. We use the proposed method to compute upper and lower bounds on the faults. These bounds are the tightest possible given the system model, input/output measurements and the bounds on the initial state and disturbances. Linear programming optimization techniques are used to obtain the bounds. Moreover, a subsequent-state-estimation technique, together with an estimation horizon update procedure are given to allow the fault detection and isolation process to be repeated in an iterative procedure. Finally, the approach is verified using a numerical example.