This paper proposes a model-based detection and isolation (FDI) system based on nonlinear state estimation that can be applied to nonlinear systems. The proposed FDI system uses an extended Kalman filter (EKF), in which conditions based on high filtering are defined to best serve the FDI objectives. A better understanding of the residual trends, calculated from the difference between measurements and the EKF estimates, can be obtained when a fault occurs by developing a model that is able to predict the behavior of the residuals. This model is utilized as the basis for detection and isolation of single and multiple faults. Comparisons with data driven techniques, specifically principal component analysis (PCA) and Kernel PCA, show superior isolation results, having the advantage of distinguishing single and multiple faults from a diverse array of possible faults, a common occurrence in complex processes. The proposed approach is validated using an experimental air heater.