Fault Diagnosis with Structured Augmented State Models: Modeling, Analysis, and Design

This paper presents an internal model approach for modeling, analysis, and diagnostic functionality design for nonlinear systems operating subject to single- and multiple-faults. We therefore provide the framework of structured augmented state models. Fault characteristics are considered to be generated by dynamical exosystems, switched via equality constraints to overcome the augmented state observability limiting the number of diagnosable faults. Conditions and practical procedures for diagnosability and fault-detectability analysis are provided and exemplified, stating essential premises for the synthesis of diagnostic functionalities. Based on the proposed model, the fault diagnosis problem is specified as an optimal hybrid augmented state estimation problem. Sub-optimal solutions are motivated and the fault diagnosis of a DC drive is illustrated. As the considered class of fault diagnosis problems is large, the suggested approach is not only of theoretical interest but also of high practical relevance