Accurate detection and isolation of faults is a critical component of a reliable and efficient plantwide fault-tolerant control system. In a recent work (Ohran et al. AIChE J. 2008, 54, 223), we demonstrated that using a nonlinear controller to enforce a specific structure in the closed-loop system allows data-based detection and isolation of certain faults that would otherwise not be isolable using data-based techniques that do not impose the necessary closed-loop system structure. In this work, it is demonstrated through a multiunit chemical process example how this approach can be applied in a plantwide setting. By using nonlinear, model-based control laws to decouple certain states from faults of interest, unique fault responses in the state trajectories are obtained in the closed-loop system. On the basis of the unique responses, fault isolation becomes possible using data-based statistical process monitoring methods. The effectiveness of the method was tested through an extensive Monte Carlo simulation study of 500 runs for each of four fault scenarios and through comparison with a conventional (proportional-integral) feedback control law.