This paper presents a novel methodology for systematically designing a fault detection, isolation, and identification algorithm for nonlinear systems with known model structure but uncertainty in parameters. The proposed fault diagnosis methodology does not require historical operational data and/or a priori fault information in order to achieve accurate fault identification. This is achieved by a two-step procedure consisting of a nonlinear observer, which includes a parameter estimator and a fault isolation and identification filter. Parameter estimation within the observer is performed by using the unknown parameters as augmented states of the system, and robustness is ensured by application of a variation of Kharitonov's theorem to the observer design. The filter design for fault reconstruction is based upon a linearization, which has to be repeatedly computed at each step where a fault is to be identified. However, this repeated linearization does not pose a severe drawback because linearization of a model can be automated and is computationally not very demanding for models used for fault detection. It is not possible to simultaneously perform parameter estimation and fault reconstruction because faults and the parametric uncertainty influence one another. Therefore, these two tasks are performed at different time scales, where the fault identification takes place at a higher frequency than the parameter estimation. It is shown that the fault can be reconstructed under some realistic assumptions and the performance of the proposed methodology is evaluated on a simulated chemical process exhibiting nonlinear dynamic behavior.