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Data-driven fault detection and isolation of nonlinear systems using deep learning for Koopman operator by Mohammadhosein Bakhtiaridoust; Meysam Yadegar; Nader Meskin is a Engineering article available to read on EtoBox.

What is Data-driven fault detection and isolation of nonlinear systems using deep learning for Koopman operator about?

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.

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Author

Mohammadhosein Bakhtiaridoust; Meysam Yadegar; Nader Meskin

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Engineering (Physical Sciences)