  This paper presents a novel observer-based approach to detect and isolate
faulty sensors in nonlinear systems. The proposed sensor fault detection and
isolation (s-FDI) method applies to a general class of nonlinear systems. Our
focus is on s-FDI for two types of faults: complete failure and sensor
degradation. The key aspect of this approach lies in the utilization of a
neural network-based Kazantzis-Kravaris/Luenberger (KKL) observer. The neural
network is trained to learn the dynamics of the observer, enabling accurate
output predictions of the system. Sensor faults are detected by comparing the
actual output measurements with the predicted values. If the difference
surpasses a theoretical threshold, a sensor fault is detected. To identify and
isolate which sensor is faulty, we compare the numerical difference of each
sensor meassurement with an empirically derived threshold. We derive both
theoretical and empirical thresholds for detection and isolation, respectively.
Notably, the proposed approach is robust to measurement noise and system
uncertainties. Its effectiveness is demonstrated through numerical simulations
of sensor faults in a network of Kuramoto oscillators.
