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AuthorBakhtiaridoust, M.
AuthorIrani, Fatemeh Negar
AuthorYadegar, Meysam
AuthorMeskin, Nader
Available date2023-03-29T11:01:46Z
Publication Date2023-01-01
Publication NameIET Control Theory and Applications
Identifierhttp://dx.doi.org/10.1049/cth2.12366
CitationBakhtiaridoust, M., Irani, F. N., Yadegar, M., & Meskin, N. (2023). Data‐driven sensor fault detection and isolation of nonlinear systems: Deep neural‐network Koopman operator. IET Control Theory & Applications, 17(2), 123-132.‏
ISSN17518644
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85141410834&origin=inward
URIhttp://hdl.handle.net/10576/41425
AbstractThis paper proposes a data-driven sensor fault detection and isolation approach for the general class of nonlinear systems. The proposed method uses deep neural network architecture to obtain an invariant set of basis functions for the Koopman operator to form a linear predictor for a nonlinear system. Then, the obtained Koopman predictor has been used in a geometric framework for sensor fault detection and isolation purposes without relying on a priori knowledge about the underlying dynamics as well as requiring faulty data, leading to a data-driven sensor fault detection and isolation framework for nonlinear systems. Finally, the approach's efficacy is demonstrated using simulation case study on a two-degree of freedom robot arm.
Languageen
PublisherJohn Wiley and Sons Inc
SubjectDeep neural networks
Degrees of freedom (mechanics)
Fault detection
TitleData-driven sensor fault detection and isolation of nonlinear systems: Deep neural-network Koopman operator
TypeArticle
Pagination123-132
Issue Number2
Volume Number17
dc.accessType Abstract Only


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