This paper develops a nonlinear observer-based approach for distributed fault detection of a class of interconnected
input–output nonlinear systems, which is robust to modeling uncertainty and measurement
noise. First, a nonlinear observer design is used to generate the residual signals required for fault detection.
Then, a distributed fault detection scheme and the corresponding adaptive thresholds are designed
based on the observer characteristics and, at the same time, filtering is used in order to attenuate the effect
of measurement noise, which facilitates less conservative thresholds and enhanced robustness. Finally, a
fault detectability condition characterizing quantitatively the class of detectable faults is derived.
