Fault detection and isolation using interval analysis: application to vehicle monitoring

This paper gives an example of the use set membership techniques for detecting component fault and model failures and isolating the cause of the fault. Set membership estimation techniques can inherently detect model failure when the estimated set becomes empty. This property is here applied for fusing parity equations generated by an analytic redundancy study. For each parity equation, one defines a symbolic indicator that individually characterizes a certain or possible failure. Defining a (cause/effect) array makes it possible to isolate the certain or possible causes of the defect. The method is developed within a pedagogical example of the kinematical model of a vehicle.