Robust fault detection using interval models

The problem of model-based fault detection in the presence of both parametric uncertainty and noise is addressed in this paper. Intervals are used to represent the uncertainty in the system parameters and interval extensions of parity equations are used as adaptive threshold selectors. A proper combination in time of different (interval) parity equations, together with a robust indicator, is used to maintain a good sensitivity to faults whilst avoiding the effect of noise. The results in the application to the detection of different faults in a DC motor are used to show the good properties of the proposed method.