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Interval Sliding Mode Observer Based Incipient Fault Detection with Application to a High-Speed Railway Traction Device

Zhang, Kangkang and Jiang, Bin and Yan, Xing-Gang and Shen, Jun and Mao, Zehui (2017) Interval Sliding Mode Observer Based Incipient Fault Detection with Application to a High-Speed Railway Traction Device. In: 2016 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS). IEEE, pp. 157-162. ISBN 978-1-5090-6085-6. E-ISBN 978-1-5090-6084-9. (doi:10.1109/IRIS.2016.8066083) (KAR id:58604)

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https://dx.doi.org/10.1109/IRIS.2016.8066083

Abstract

In this paper, a novel interval sliding mode observer is designed to detect incipient faults for a class of non-Lipschitz nonlinear systems with mismatched uncertainties. The interval estimation concept is introduced to design interval estimator for the nonlinear subsystem with uncertainties bounded by known intervals. Then novel injection functions are designed to ensure that the sliding motion takes place and maintains thereafter. At last, new residual generators and adaptive threshold generators are designed, and the corresponding fault detectability is studied. Case study on a traction device in CRH (China Railway High-Speed) is presented to demonstrate the effectiveness of proposed incipient fault detection scheme.

Item Type: Book section
DOI/Identification number: 10.1109/IRIS.2016.8066083
Uncontrolled keywords: observers; uncertainty; fault detection; nonlinear systems; fault diagnosis; generators; intelligent sensors
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Xinggang Yan
Date Deposited: 14 Nov 2016 10:16 UTC
Last Modified: 16 Feb 2021 13:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58604 (The current URI for this page, for reference purposes)
Yan, Xing-Gang: https://orcid.org/0000-0003-2217-8398
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