ROBUST FAULT DETECTION AND ISOLATION BASED ON THE KULLBACK DIVERGENCE

In his paper, a fully stochastic setting is proposed to design indicators aimed at detecting and isolating faults in a dynamical system. Modeling uncertainties, as well as process and measurement noise are accounted for. Two distinct sets of fault indicators are determined, one for fault detection, he other one for fault isolation. Each indicator is obtained by solving an optimization problem aimed at maximizing a statistical distance (namely he Kullback divergence) between he distribution of the indicator under different faulty and fault free working modes. The solution of he problem relies on statistical experiment design for he stochastic characterization of he system.