Data-driven fault detection and isolation of nonlinear systems using deep learning for Koopman operator.

This paper proposes a data-driven actuator fault detection and isolation approach for the general class of nonlinear systems. The proposed method uses a deep neural network architecture to obtain an invariant set of basis functions for the Koopman operator to form a linear Koopman predictor for a nonlinear system. Then, the obtained linear model is used for fault detection and isolation purposes without relying on prior knowledge about the underlying dynamics. Moreover, a recursive method is proposed for fault detection and isolation that is entirely data-driven with the key feature of global validity for the system's whole operating region due to the Koopman operator's global characteristic. Finally, the approach's efficacy is demonstrated using two simulations on a coupled nonlinear system and a two-link manipulator benchmark. • A novel data-driven DMD-based Koopman FDI (K-FDI) method is presented for detecting and isolating actuator faults. • A new Koopman-based state-preserving DNN architecture is proposed for actuated systems. • The nonlinear FDI problem is formulated as linear FDI using Koopman operator. • The presented Koopman FDI method is extended to be computed incrementally for real-time situation. [ABSTRACT FROM AUTHOR]