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This paper presents a novel observer-based approach to detect and isolatefaulty sensors in nonlinear systems. The proposed sensor fault detection andisolation (s-FDI) method applies to a general class of nonlinear systems. Ourfocus is on s-FDI for two types of faults: complete failure and sensordegradation. The key aspect of this approach lies in the utilization of aneural network-based Kazantzis-Kravaris/Luenberger (KKL) observer. The neuralnetwork is trained to learn the dynamics of the observer, enabling accurateoutput predictions of the system. Sensor faults are detected by comparing theactual output measurements with the predicted values. If the differencesurpasses a theoretical threshold, a sensor fault is detected. To identify andisolate which sensor is faulty, we compare the numerical difference of eachsensor meassurement with an empirically derived threshold. We derive boththeoretical and empirical thresholds for detection and isolation, respectively.Notably, the proposed approach is robust to measurement noise and systemuncertainties. Its effectiveness is demonstrated through numerical simulationsof sensor faults in a network of Kuramoto oscillators.

Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers