Set-based fault detection and isolation

Abstract

Model-based fault detection of dynamic processes is based on the use of models (i.e. analytical redundancy) to check the consistency of observed behaviors. The robustness of a fault diagnosis algorithm is the degree of sensitivity to faults compared to the degree of sensitivity to uncertainty. Research on robust fault diagnosis methods has been very active in the fault detection and isolation (FDI) community these past years. This chapter reviews the passive approach when considering the nominal model plus the uncertainty on every parameter bounded by intervals. This type of uncertainty modeling provides a type of models known as interval models. The chapter introduces some background materi..