Robust Fault Diagnosis of Nonlinear Systems Using Interval Constraint Satisfaction and Analytical Redundancy Relations

In this paper, a robust fault diagnosis problem for nonlinear systems considering both bounded parametric modeling errors and noise is addressed using parity-equation-based analytical redundancy relations (ARR) and interval constraint satisfaction techniques. Fault detection, isolation, and estimation tasks are considered. Moreover, the problem of quantifying the uncertainty in the ARR parameters is also addressed. To illustrate the usefulness of the proposed approach, a case study based on the well-known wind turbine benchmark is used.