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.

翻译：本文提出了一种基于观测器的非线性系统传感器故障检测与隔离新方法。所提出的传感器故障检测与隔离方法适用于一般非线性系统类别。我们重点关注两类故障的传感器故障检测与隔离：完全失效与性能退化。该方法的核心在于利用基于神经网络的Kazantzis-Kravaris/Luenberger观测器。该神经网络通过学习观测器动力学特性，能够准确预测系统的输出值。通过比较实际输出测量值与预测值实现传感器故障检测：若差值超过理论阈值，则判定传感器发生故障。为了识别并隔离故障传感器，我们将各传感器测量值的数值差异与经验推导的阈值进行比较。我们分别推导了用于检测和隔离的理论阈值与经验阈值。值得注意的是，所提方法对测量噪声和系统不确定性具有鲁棒性。通过在Kuramoto振荡器网络中对传感器故障进行数值仿真，验证了该方法的有效性。