Guaranteed diagnosability of parametric faults in nonlinear systems

In this work we focus on unique diagnosability of parametric faults in the presence of measurement uncertainty and model mismatches. Specifically, we formulate a condition for diagnosability of parametric faults in a set-based framework that allows for direct consideration of uncertainty. Based on this condition we present an approach for the analysis and certification of diagnosability. Furthermore, we propose an approach for the redesign of initially given fault classifications in the parameter space. Specifically we compute diagnosable subsets of initially given parameter sets in polynomial discrete-time fault candidates by comparing pairs of fault candidates. Furthermore, we demonstrate the presented approach for a numerical example.